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								Grad. Inst. of Networking &amp; Multimedia, Nat. Taiwan Univ., Taipei, Taiwan 
								
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								Sch. of Comput. Sci., Simon Fraser Univ., Burnaby, BC, Canada 
								
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								Sch. of Software &amp; Microelectron., Peking Univ., Beijing, China 
								
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								Beijing Key Lab. of Intell. Telecommun. Software &amp; Multimedia, Beijing Univ. of Posts &amp; Telecommun., Beijing, China 
								
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								Dept. of Comput. Sci., Univ. of California, Davis, CA, USA 
								
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								Nat. Univ. of Defense Technol., Changsha, China 
								
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								Dept. of Inf. Manage., Nat. Univ. of Kaohsiung, Kaohsiung, Taiwan 
								
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								Comput. Lab., Univ. of Cambridge, Cambridge, UK 
								
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								Coll. of Inf. Sci. &amp; Technol., Nanjing Univ. of Aeronaut. &amp; Astronaut., Nanjing, China 
								
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								Sch. of Comput. Sci. &amp; Inf., Univ. Coll. Dublin, Dublin, Ireland 
								
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				<h3>           
					
						
						
							
								
								
										
									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5210166&contentType=Journals+%26+Magazines&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Automatic Role Recognition in Multiparty Recordings: Using Social Affiliation Networks for Feature Extraction
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						Salamin, H.; Favre, S.; Vinciarelli, A. 
					
					
						
			              	               
			                <a href="http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6046">
			               		<br>Multimedia, IEEE Transactions on
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						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/TMM.2009.2030740" target="blank">
									10.1109/TMM.2009.2030740
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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										Automatic analysis of social interactions attracts increasing attention in the multimedia community. This letter considers one of the most important aspects of the problem, namely the roles played by individuals interacting in different settings. In particular, this work proposes an automatic approach for the recognition of roles in both production environment contexts (e.g., news <span class="snippet">and</span> talk-shows) <span class="snippet">and</span> spontaneous situations (e.g., meetings). The experiments are performed over roughly 90 h of material (one of the largest databases used for role recognition in the literature) <span class="snippet">and</span> show that the recognition effectiveness depends on how much the roles influence the behavior of people. Furthermore, this work proposes the first approach for modeling mutual dependences between roles <span class="snippet">and</span> assesses its effect on role recognition performance.
										
						                	
						                	
						                	
						                			
						             				
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										Community Aware Personalized Web Search
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				</h3>
				
					
						Shafiq, O.; Alhajj, R.; Rokne, J.G. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5562738">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2010 International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/ASONAM.2010.85" target="blank">
									10.1109/ASONAM.2010.85
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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									<p>
										Searching for the right information over the Web is not straight-forward. In the era of high speed internet, high capacity networks, <span class="snippet">and</span> interactive Web applications, it has become even easier for the users to publish data <span class="snippet">online</span>. A huge amount of data is published over the internet; every data is in the form of web pages, news, blogs <span class="snippet">and</span> other material, etc. Similarly, for search engines like Google <span class="snippet">and</span> Yahoo, it becomes rather hard to find out the right information, i.e., as per user's preferences; search results for same query differ in priority for different users. In this paper, we proposed a way to prioritize search results of search engines like Google, based on the personal interests <span class="snippet">and</span> context of users. In order to find out personal interest <span class="snippet">and</span> context, we follow a unique approach of (1) finding out activities of a user of his/her social-network, (2) finding out what information does the social networks (i.e., friends <span class="snippet">and</span> community) provide to the user. Based on this information, we have developed a methodology that takes into account the information about social networks <span class="snippet">and</span> prioritize search results from Web search engine.
										
						                	
						                	
						                	
						                			
						             				
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										A vector space model approach to social relation extraction from text corpus
									</a>
								
							
						
					
				</h3>
				
					
						Xin Guo; Yang Xiang; Qian Chen 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6012258">
			                	<br>Fuzzy Systems <span class="snippet">and</span> Knowledge Discovery (FSKD), 2011 Eighth International Conference on
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		       			<br>  
						Volume:	3					
					
						
						
						
					
					
										 	
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								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/FSKD.2011.6019806" target="blank">
									10.1109/FSKD.2011.6019806
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										<span class="snippet">Social network analysis</span> (SNA) is significantly about research of social relations as well as structure in social environment. All the dependencies between people, organizations <span class="snippet">and</span> environment constitute one or several networks. The author presented a vector space model (VSM) approach to extract <span class="snippet">and</span> represent relations from text corpus. The approach employed VSM to represent the weight value of every social object's frequency in every text. It reflected the relationships between social objects <span class="snippet">and</span> text corpus. Then the vector space is decomposed into a new vector space reflecting the relationships between social objects. It is concluded that the application of VSM can obtain deeper social relations hid in text <span class="snippet">and</span> text corpus <span class="snippet">and</span> increase the effect <span class="snippet">and</span> efficiency of <span class="snippet">social network analysis</span> of text corpus.
										
						                	
						                	
						                	
						                			
						             				
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										AHSCAN: Agglomerative Hierarchical Structural Clustering Algorithm for Networks
									</a>
								
							
						
					
				</h3>
				
					
						Yuruk, N.; Mete, M.; Xiaowei Xu; Schweiger, T.A.J. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/ASONAM.2009.74" target="blank">
									10.1109/ASONAM.2009.74
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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									<p>
										Many systems in sciences, engineering <span class="snippet">and</span> nature can be modeled as networks. Examples include the Internet, WWW <span class="snippet">and</span> social networks. Finding hidden structures is important for making sense of complex networked data. In this paper we present a new network clustering method that can find clusters in an agglomerative fashion using structural similarity of vertices in the given network. Experiments conducted on real datasets demonstrate promising performance of the new method.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5231947&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Getting <span class="snippet">Online</span> but Still Living Offline: The Complex Relationship of Technology Adoption <span class="snippet">and</span> In-person Social Networks
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				</h3>
				
					
						Putnam, C.; Kolko, B. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2009.75" target="blank">
									10.1109/ASONAM.2009.75
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										Previous research in Kyrgyzstan has demonstrated the importance of close social networks as avenues for gathering <span class="snippet">and</span> sharing information, assistance <span class="snippet">and</span> goods in the country. However, the relationship between technology use <span class="snippet">and</span> social network use has not been investigated; understanding this relationship is important when considering the design of technology applications that support existing social networks. Contrary to Robert Putnampsilas hypothesis that technologies have an "individualizing" force on populations, we have found that social network patterns do not appear to be negatively associated with higher technology use. In fact, the higher the level of technology use, the more face-to-face social networks were used for multiple types of information seeking.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6181988&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Social network link predict based on AF model
									</a>
								
							
						
					
				</h3>
				
					
						Cheng Chang; Xiying Yao 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6175418">
			                	<br>Computer Science <span class="snippet">and</span> Network Technology (ICCSNT), 2011 International Conference on
			                </a>
						
							
						
							
												
					
						
					
		       			<br>  
						Volume:	1					
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								<a href="http://dx.doi.org/10.1109/ICCSNT.2011.6181988" target="blank">
									10.1109/ICCSNT.2011.6181988
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										With the development of the Internet, social network has deeply rooted. People could construct more widely contact through social network. At the same time, <span class="snippet">social network analysis</span> has made in-depth development; it has been widely used in modern sociology, anthropology, economics, <span class="snippet">and</span> biology area. In social network, everyone has his wish to expand his circle of communication. But their energy of digging the potential friends by themselves is limited, so the link prediction which is one of sub-tasks of <span class="snippet">social network analysis</span> becomes increasingly important. This article uses the AF model for link prediction to social network, Verify the correctness <span class="snippet">and</span> validity of the method through the experiment on Enron's e-mail data collection.
										
						                	
						                	
						                	
						                			
						             				
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										Do Developers Introduce Bugs When They Do Not Communicate? The Case of Eclipse <span class="snippet">and</span> Mozilla
									</a>
								
							
						
					
				</h3>
				
					
						Bernardi, M.L.; Canfora, G.; Di Lucca, G.A.; Di Penta, M.; Distante, D. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6178852">
			                	<br>Software Maintenance <span class="snippet">and</span> Reengineering (CSMR), 2012 16th European Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/CSMR.2012.24" target="blank">
									10.1109/CSMR.2012.24
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2012
							
										
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										Developers working on related artifacts often communicate each other to coordinate their changes <span class="snippet">and</span> to make others aware of their changes. When such a communication does not occur, this could create misunderstanding <span class="snippet">and</span> cause the introduction of bugs. This paper investigates how the level of communication between committers relates to their proneness to introduce faults. This is done by identifying committers likely responsible of bug-introducing changes, <span class="snippet">and</span> comparing - through social network measures - characteristics of their communication with the characteristics of other committers. We report results from a study conducted on bugs from Eclipse <span class="snippet">and</span> Mozilla, indicating that bug-introducing committers have a higher social importance than other committers, although the communication between themselves is significantly lower than for others.
										
						                	
						                	
						                	
						                			
						             				
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										Differential Effects of the Volume <span class="snippet">and</span> Diversity of Communication Network Ties on Knowledge Workers' Performance
									</a>
								
							
						
					
				</h3>
				
					
						Burton, P.; Yu Wu; Prybutok, V.R.; Harden, G. 
					
					
						
			              	               
			                <a href="http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=47">
			               		<br>Professional Communication, IEEE Transactions on
			                </a>					
						
						
							
						
							
												
					
						
					
		       			<br>  
						Volume:	55					
					
						
					
						,<a href="http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=6276341">
							Issue: 3	
						</a>						
						
						
					
					
										 	
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967026">
								Aerospace</a>
						   
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								Bioengineering</a>
						   
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								Communication, Networking &amp; Broadcasting</a>
						   
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								Fields, Waves &amp; Electromagnetics</a>
						   
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								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
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								Nuclear Engineering</a>
						   
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								Power, Energy, &amp; Industry Applications</a>
						   
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								Signal Processing &amp; Analysis</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967025">
								Transportation</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/TPC.2012.2190345" target="blank">
									10.1109/TPC.2012.2190345
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2012
							
										
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									<p>
										Research problem: For knowledge workers, such as IT professionals, the ties within their social network are a major channel for communicating the requisite knowledge <span class="snippet">and</span> information. While prior studies tended to favor higher network centrality (i.e., larger volume of network ties), in this study, the researchers argue that centrality can reduce communication efficiency if the diversity of the ties is low. Research question: Which characteristic of communication ties, volume or diversity, has more influence on knowledge workers' performance? Literature review: Using <span class="snippet">social network analysis</span> (SNA) as the theoretical framework, a review of the literature shows that social network ties have important performance implications because they can enhance an individual's access to valued resources. Studies have also examined the performance impact of “hindrance network centrality,” or how frequently a person is described by other network members as a hindrance to their performance. However, current research has overlooked the possible negative communication consequences of centrality in regards to redundant information, which may negatively affect job performance. Methodology: The current study employs a quantitative approach, using the standard SNA method of a “name generator” questionnaire to collect network data. Participants were 98 people in the IT department at a large defense company. We identified 3905 relationships <span class="snippet">and</span> analyzed them with the UCINET software. Subsequently, social network variables, as well as performance <span class="snippet">and</span> human capital variables were analyzed in logistic regressions. Results <span class="snippet">and</span> discussion: Results showed a significant relationship between constraint, a measure of tie diversity, <span class="snippet">and</span> performance. Centrality, the measure of tie volume, however, was not significantly related to performance. Our findings about the volume versus diversity of communication ties have important implications for professional communicators - n the age of fast-growing social network media. A limitation of our study is that we approximated communication ties with social network links. We suggest that future studies further validate our findings by using a more direct measure of communication ties.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5231874&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Mining the Dynamics of Music Preferences from a Social Networking Site
									</a>
								
							
						
					
				</h3>
				
					
						Schlitter, N.; Falkowski, T. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
						   	;
						   
						
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2009.26" target="blank">
									10.1109/ASONAM.2009.26
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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							243	- 248
													
													
						
						
						 
						
					
					
					
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										In this paper we present an application of our incremental graph clustering algorithm (DENGRAPH) on a data set obtained from the music community site Last.fm. The aim of our study is to determine the music preferences of people <span class="snippet">and</span> to observe how the taste in music changes over time. Over a period of 130 weeks, we extract for each interval user profiles of 1,800 users that represent their music listening behavior. By building <span class="snippet">and</span> incrementally clustering a graph of similar users, we obtain groups of people with similar music preferences. We label these clusters with genres according to the user profiles of the cluster members. Due to the incremental nature of DENGRAPH we show how clusters evolve over time. Besides the growth <span class="snippet">and</span> decrease of clusters we observe how new clusters emerge <span class="snippet">and</span> old clusters die. Furthermore, we show the merge <span class="snippet">and</span> split of clusters. The results of our experiments indicate that DENGRAPH is particularly useful to efficiently detect groups of similar users <span class="snippet">and</span> to track them over time.
										
						                	
						                	
						                	
						                			
						             				
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										A social network representation for Collaborative Filtering Recommender Systems
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						Perez, L.G.; Chiclana, F.; Ahmadi, S. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6112291">
			                	<br>Intelligent Systems Design <span class="snippet">and</span> Applications (ISDA), 2011 11th International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								<a href="http://dx.doi.org/10.1109/ISDA.2011.6121695" target="blank">
									10.1109/ISDA.2011.6121695
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						 <br>Publication Year: 2011
							
										
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										Collaborative Filtering Recommender Systems are one of the most used <span class="snippet">and</span> well-known tools in the e-commerce area because they are adaptive <span class="snippet">and</span> they do not need information about the recommended items. Although many studies have been proposed to take advantage of the information gathered by this kind of recommender systems, none have focused on the use of <span class="snippet">social network analysis</span>. In this contribution we present a first approach that shows some of the advantages <span class="snippet">and</span> results that can be obtained applying this methodology.
										
						                	
						                	
						                	
						                			
						             				
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										Detecting Structural Changes <span class="snippet">and</span> Command Hierarchies in Dynamic Social Networks
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						Bourqui, R.; Gilbert, F.; Simonetto, P.; Zaidi, F.; Sharan, U.; Jourdan, F. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
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					 	<br>Topic(s):&nbsp;
					 	
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								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/ASONAM.2009.55" target="blank">
									10.1109/ASONAM.2009.55
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						 <br>Publication Year: 2009
							
										
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										Community detection in social networks varying with time is a common yet challenging problem whereby efficient visualization of evolving relationships <span class="snippet">and</span> implicit hierarchical structure are important task. The main contribution of this paper is towards establishing a framework to analyze such social networks. The proposed framework is based on dynamic graph discretization <span class="snippet">and</span> graph clustering.The framework allows detection of major structural changes over time, identifies events analyzing temporal dimension <span class="snippet">and</span> reveals command hierarchies in social networks.We use the Catalano/Vidro dataset for empirical evaluation <span class="snippet">and</span> observe that our framework provides a satisfactory assessment of the social <span class="snippet">and</span> hierarchical structure present in the dataset.
										
						                	
						                	
						                	
						                			
						             				
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										Personalized Email Prioritization Based on Content <span class="snippet">and</span> <span class="snippet">Social Network Analysis</span>
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						Yiming Yang; Shinjae Yoo; Lin, F.; Il-Chul Moon 
					
					
						
			              	               
			                <a href="http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9670">
			               		<br>Intelligent Systems, IEEE
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						Volume:	25					
					
						
					
						,<a href="http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=5552580">
							Issue: 4	
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
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								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/MIS.2010.56" target="blank">
									10.1109/MIS.2010.56
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						 <br>Publication Year: 2010
							
										
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										The proposed system combines unsupervised clustering, <span class="snippet">social network analysis</span>, semisupervised feature induction, <span class="snippet">and</span> supervised classification to model user priorities among incoming email messages.
										
						                	
						                	
						                	
						                			
						             				
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										Establishing New Friendships-from Face-to-Face to Facebook: A Case Study of College Students
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						Bau-Min Tu; Hsiao-Chi Wu; Chingcha Hsieh; Pin-Hung Chen 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5716643">
			                	<br>System Sciences (HICSS), 2011 44th Hawaii International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/HICSS.2011.192" target="blank">
									10.1109/HICSS.2011.192
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										Facebook, the largest social networking site in terms of registered users, has attracted the attention of many researchers. However, most studies on Facebook focus on the relationships between personalities <span class="snippet">and</span> user behaviors, <span class="snippet">and</span> few studies have examined how Facebook influences the establishment of friendship in real life. Therefore, this study undertakes a case study of 36 college students, <span class="snippet">and</span> applies a <span class="snippet">social network analysis</span> to investigate the differences between face-to-face (FTF) <span class="snippet">and</span> Facebook friendship networks. It also examines qualitative data to explain the reasons for any variances. The results indicate that there are two significant differences-gender gaps <span class="snippet">and</span> peer group pressures-between the two types of friendship networks. The discussion explains why gender gaps <span class="snippet">and</span> peer group pressures are factors that cause barriers to FTF friendships. It also elaborates on how Facebook promotes the establishment of new friendships.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5693457&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Meerkat: Community Mining with Dynamic Social Networks
									</a>
								
							
						
					
				</h3>
				
					
						Jiyang Chen; Fagnan, J.; Goebel, R.; Rabbany, R.; Sangi, F.; Takaffoli, M.; Verbeek, E.; Zaïane, O. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5691154">
			                	<br>Data Mining Workshops (ICDMW), 2010 IEEE International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966918">
								Engineering Profession</a>
						   
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								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
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								Geoscience</a>
						   
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								Power, Energy, &amp; Industry Applications</a>
						   
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								Robotics &amp; Control Systems</a>
						   
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								Signal Processing &amp; Analysis</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967025">
								Transportation</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ICDMW.2010.40" target="blank">
									10.1109/ICDMW.2010.40
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										Meerkat is a tool for visualization <span class="snippet">and</span> community mining of social networks. It is being developed to offer novel algorithms <span class="snippet">and</span> functionality that other tools do not possess. Meerkat's features include navigation through graphical representations of networks, network querying <span class="snippet">and</span> filtering, a multitude of graphical layout algorithms, community mining using recently developed algorithms, <span class="snippet">and</span> dynamic network event analysis using recently published algorithms. These features will allow more insightful exploratory analysis <span class="snippet">and</span> more robust inferences about communities <span class="snippet">and</span> the significance of entity relationships. Meerkat is under active development, <span class="snippet">and</span> future features will include additional options for community mining <span class="snippet">and</span> visualization, focusing on algorithms <span class="snippet">and</span> user interface designs not existing in other <span class="snippet">social network analysis</span> tools.
										
						                	
						                	
						                	
						                			
						             				
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				<h3>           
					
						
						
							
								
								
										
									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6298852&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Detecting Key Players in Terrorist Networks
									</a>
								
							
						
					
				</h3>
				
					
						Berzinji, A.; Kaati, L.; Rezine, A. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6298753">
			                	<br>Intelligence <span class="snippet">and</span> Security Informatics Conference (EISIC), 2012 European
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
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								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/EISIC.2012.13" target="blank">
									10.1109/EISIC.2012.13
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2012
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										The interest in analyzing loosely connected <span class="snippet">and</span> decentralized terrorist networks of global reach has grown during the past decade. <span class="snippet">Social Network Analysis</span> (SNA) is one approach towards understanding terrorist networks since it can be used to analyze the structure of a network <span class="snippet">and</span> to detect important persons <span class="snippet">and</span> links. In this work we study decentralized terrorist networks with different types of nodes. The nodes can be either organizations, places or persons. We use a combination of different centrality measures to detect key players in such networks.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=4937501&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Assessing organizational stability via network analysis
									</a>
								
							
						
					
				</h3>
				
					
						Collingsworth, B.; Menezes, R.; Martins, P. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4911405">
			                	<br>Computational Intelligence for Financial Engineering, 2009. CIFEr '09. IEEE Symposium on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
						   	;
						   
						
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
						   	;
						   
						
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966917">
								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966781">
								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/CIFER.2009.4937501" target="blank">
									10.1109/CIFER.2009.4937501
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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							43	- 50
													
													
						
						
						 
						
					
					
					
			
						
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				IEEE Conference Publications
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										It is widely known that the email system forms a social network. Analysis of email networks reveals properties similar to classic social networks such as friendship or academic collaboration networks. Like other social networks, the properties observed in email networks are the result of patterns of human social behavior rather than the underlying technology. Hence, email social network properties correlate to the social environment in which they are generated. The overall social behavior observed in an organization may be attributed directly to organizational stability <span class="snippet">and</span> robustness. As a result, organizational health <span class="snippet">and</span> robustness may be discerned by examining the social network properties of the network formed by the email interaction of its employees because they certainly reflect changes in organizational mood. The fears, worries, gossips, the good <span class="snippet">and</span> the bad, are reflected in the email activity of individuals in the organization; the challenge though is to extract his information from the network itself. In this paper we provide a first step in the process of demonstrating that email <span class="snippet">social network analysis</span> can tell us more about the organization than we may think; we show using a case study based on the Enron corporation that problems in the organization were apparent as an emergent characteristic of social network formed by email exchange in the organization.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5988976&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Analysis of the Core Team Role in Open Source Communities
									</a>
								
							
						
					
				</h3>
				
					
						Torres, M.R.M.; Toral, S.L.; Perales, M.; Barrero, F. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5986779">
			                	<br>Complex, Intelligent <span class="snippet">and</span> Software Intensive Systems (CISIS), 2011 International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967046">
								Components, Circuits, Devices &amp; Systems</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/CISIS.2011.25" target="blank">
									10.1109/CISIS.2011.25
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										Open source software represents a new paradigm of software development based on a subjacent community. It is widely accepted in the literature the layered structure of open source communities, being the core group the most active contributors usually located at the center of the community. The tasks of this group include not only an intense activity in terms of contributions but also to promote participation among the rest of the community members. In this paper, the general role of this group is analyzed by modeling communities as Social Networks <span class="snippet">and</span> applying <span class="snippet">Social Network Analysis</span> techniques. Findings related their brokerage activity with open source software success.
										
						                	
						                	
						                	
						                			
						             				
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				<h3>           
					
						
						
							
								
								
										
									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5283887&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Centrality <span class="snippet">and</span> Power among Website Users: A <span class="snippet">Social Network Analysis</span> Application
									</a>
								
							
						
					
				</h3>
				
					
						McGrath, G.M. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5282954">
			                	<br>Computational Science <span class="snippet">and</span> Engineering, 2009. CSE '09. International Conference on
			                </a>
						
							
						
							
												
					
						
					
		       			<br>  
						Volume:	4					
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966781">
								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/CSE.2009.190" target="blank">
									10.1109/CSE.2009.190
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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							562	- 567
													
													
						
						
						 
						
					
					
					
			
						
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										A major feature of the emergence of Web 2.0 is the proliferation of dasiaconsumer-generated contentpsila (CGC) websites, often established as alternatives to corporate or dasiaofficialpsila sites. In this paper, we report on a preliminary study designed to demonstrate how techniques drawn from dasiaSocial Network Analysispsila (SNA) <span class="snippet">and</span> artificial intelligence might be employed to develop an understanding of the influence relationships <span class="snippet">and</span> hierarchies within these websites. Armed with this knowledge, organizations may be able to develop strategies as to how they might best co-exist <span class="snippet">and</span> deal with relevant CGC sites.
										
						                	
						                	
						                	
						                			
						             				
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										Lifespan <span class="snippet">and</span> popularity measurement of <span class="snippet">online</span> content on social networks
									</a>
								
							
						
					
				</h3>
				
					
						Beiming Sun; Ng, V.T. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5975309">
			                	<br>Intelligence <span class="snippet">and</span> Security Informatics (ISI), 2011 IEEE International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294961108">
								Bioengineering</a>
						   
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								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
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								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
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								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ISI.2011.5984118" target="blank">
									10.1109/ISI.2011.5984118
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										With rapid development <span class="snippet">and</span> increased popularity of social networks, more interests have been made in obtaining information from such social networking websites. Analysis on the popularity of <span class="snippet">online</span> contents is one of the hottest interests which has triggered intensive research. Our research focuses on measuring the popularity of <span class="snippet">online</span> posts within specified topics, such as “drug abuse”, as it can be used to detect the crime <span class="snippet">and</span> discover potential drug abusers. We measure the lifespan <span class="snippet">and</span> the popularity of drug related posts in order to know the level of influence they made. Identifying popular posts <span class="snippet">online</span> can help us reveal the trend, <span class="snippet">and</span> also detect the latent danger <span class="snippet">and</span> may prevent future crime. In this paper, the Comment Arrival Model is proposed to identify the lifespan <span class="snippet">and</span> the comment frequency pattern of posts, which are considered the main factors to define post popularity. <span class="snippet">And</span> we present 4 general models to measure the popularity of posts which can be applied in different social network platforms. We also did experiments <span class="snippet">and</span> evaluated the performance of models in two popular social networks in Hong Kong, HK Discussion <span class="snippet">and</span> Twitter.
										
						                	
						                	
						                	
						                			
						             				
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										Network Structure of Innovation: Can Brokerage or Closure Predict Patent Quality?
									</a>
								
							
						
					
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						Jyun-Cheng Wang; Cheng-Hsin Chiang; Shu-Wei Lin 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4755313">
			                	<br>System Sciences, 2009. HICSS '09. 42nd Hawaii International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/HICSS.2009.322" target="blank">
									10.1109/HICSS.2009.322
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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									<p>
										Patents are important intellectual assets. Approaches to evaluating patent quality are mostly a posteriori uses of factual information of patent quality. This paper examines whether patent quality can be predicted a priori, i.e., at an early stage after a patent is granted by analyzing information embedded in a network of patent citations. <span class="snippet">Social network analysis</span> was applied to analyze two complementary network positions occupied by a patent - brokerage <span class="snippet">and</span> closure - to determine whether either position predicts patent quality. Differences in patent citation counts <span class="snippet">and</span> maintenance years were adopted as surrogates of patent quality. The analytical results showed that higher brokerage value can lead to higher increments of patent citation counts; higher closure value can lead to longer maintenance. The interactive effect of brokerage <span class="snippet">and</span> closure position was confirmed. These analytical results imply that a company should focus on developing patents that bridge different technologies as technology development reaches maturity.
										
						                	
						                	
						                	
						                			
						             				
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										Evaluating Quality of Health Information Sources
									</a>
								
							
						
					
				</h3>
				
					
						Weitzel, L.; Quaresma, P.; de Oliveira, J.P.M. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6184368">
			                	<br>Advanced Information Networking <span class="snippet">and</span> Applications (AINA), 2012 IEEE 26th International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967046">
								Components, Circuits, Devices &amp; Systems</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/AINA.2012.41" target="blank">
									10.1109/AINA.2012.41
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2012
							
										
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							655	- 662
													
													
						
						
						 
						
					
					
					
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										The Web is an important source for people who are seeking healthcare information. Users, who search for health information <span class="snippet">online</span>, do so without professional guidance. A major problem faced is the possibility that poor information has detrimental effects on health. In this sense, the main goal of this work is to provide a framework that evaluates health web page designed especially for common users, those that may lack sufficient knowledge to validate health content. In order to achieve this goal, we proposed a new methodology to calculate a Trust rank based on reputation <span class="snippet">and</span> a set of quality indicators. The proposed methodology has shown effective to evaluate the quality of health information sources.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5231950&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Exploratory Study of Intra-Organizational Learning from Social Network Perspective within a Spanish Knowledge Intensive Company
									</a>
								
							
						
					
				</h3>
				
					
						de Pablo Gonzalez del Campo, J.D.S.; Skerlavaj, M.; Gomez, F.G.; Dimovski, V. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2009.79" target="blank">
									10.1109/ASONAM.2009.79
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										Organizational learning is a research topic very relevant in strategic studies. Nevertheless, the way in which learning takes place in the firm constitutes one of the main gaps in research. For this reason, this paper wants to explain how learning occurs in a company. A case study of a Spanish high-tech company is used to explore the network perspective on intra-organizational learning. It expands the generalization of the network perspective to intra-organizational learning, as proposed by Skerlavaj <span class="snippet">and</span> Dimovski (2006). We formulated four propositions based on an exploratory <span class="snippet">social network analysis</span>. We provide additional arguments for the generalization of the research findings related to the importance of industry experience <span class="snippet">and</span> company tenure as determinants of the key players in learning networks. The novel contribution of this study relates to the complementarity of knowledge as a tie generator <span class="snippet">and</span> shows the importance of cross-departmental knowledge flows in projects. Moreover, similarity in terms of experiential level <span class="snippet">and</span> physical proximity creates opportunities to learn. From a practice-oriented perspective, this study offers tools for detecting the most important employees in a firm from a learning viewpoint.
										
						                	
						                	
						                	
						                			
						             				
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										Approximation <span class="snippet">and</span> Inapproximation for the Influence Maximization Problem in Social Networks under Deterministic Linear Threshold Model
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						Zaixin Lu; Wei Zhang; Weili Wu; Bin Fu; Dingzhu Du 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5960724">
			                	<br>Distributed Computing Systems Workshops (ICDCSW), 2011 31st International Conference on
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								<a href="http://dx.doi.org/10.1109/ICDCSW.2011.33" target="blank">
									10.1109/ICDCSW.2011.33
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						 <br>Publication Year: 2011
							
										
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										Abstract-Influence Maximization is the problem of finding a certain amount of people in a social network such that their aggregation influence through the network is maximized. In the past this problem has been widely studied under a number of different models. In 2003, Kempe et al. gave a (1 - 1/ϵ) approximation algorithm for the linear threshold model <span class="snippet">and</span> the independent cascade model, which are the two main models in the <span class="snippet">social network analysis</span>. In addition, Chen et al. proved that the problem of exactly computing the influence given a seed set in the two models is #P-hard. Both the linear threshold model <span class="snippet">and</span> the independent cascade model are based on randomized propagation. However such information might be obtained by surveys or data mining techniques, which makes great difference on the properties of the problem. In this paper, we study the Influence Maximization problem in the deterministic linear threshold model. As a contrast, we show that in the deterministic linear threshold model, there is no polynomial time n<sup>1-ϵ</sup>-approximation unless P=NP even at the simple case that one person needs at most two active neighbors to become active. This inapproximability result is derived with self-contained proofs without using PCP theorem. In the case that a person can be activated when one of its neighbors become active, there is a polynomial time ϵ/(ϵ-1)-approximation, <span class="snippet">and</span> we prove it is the best possible approximation under a reasonable assumption in the complexity theory, NP ⊄ DTIME(n<sup>loglogn</sup>). We also show that the exact computation of the final influence given a seed set can be solved in linear time in the deterministic linear threshold model. The Least Seed Set problem, which aims to find a seed set with least number of people to activate all the required people in a given social network, is discussed. Using an analysis framework based on Set Cover, we show a O(logn)-approximation in the case that a peop- - le become active when one of its neighbors is activated.
										
						                	
						                	
						                	
						                			
						             				
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						Wasserman, S. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5562738">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2010 International Conference on
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								<a href="http://dx.doi.org/10.1109/ASONAM.2010.89" target="blank">
									10.1109/ASONAM.2010.89
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						 <br>Publication Year: 2010
							
										
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										A Hybrid Framework for Building a Web-Page Recommender System
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						Anastopoulos, V.; Karampelas, P.; Kalagiakos, P.; Alhajj, R. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6059524">
			                	<br>Intelligence <span class="snippet">and</span> Security Informatics Conference (EISIC), 2011 European
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								<a href="http://dx.doi.org/10.1109/EISIC.2011.40" target="blank">
									10.1109/EISIC.2011.40
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						 <br>Publication Year: 2011
							
										
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										Recommender systems aim to facilitate World Wide Web users against information <span class="snippet">and</span> product overloading. They are usually intermediate programs that try to predict users' preferences <span class="snippet">and</span> items of their interest. In this paper, we present a hybrid framework that uses open source information such as web logs in combination with <span class="snippet">social network analysis</span> <span class="snippet">and</span> data mining, to extract useful information about users browsing patterns <span class="snippet">and</span> construct a recommendation engine. A case study based on real data from an organization of 250 employees is presented <span class="snippet">and</span> a system prototype is constructed based on the results.
										
						                	
						                	
						                	
						                			
						             				
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										Temporal Community Structure Patterns in Diabetes Social Networks
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						Chomutare, Taridzo; Arsand, Eirik; Hartvigsen, Gunnar 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6423126">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2012 IEEE/ACM International Conference on
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								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/ASONAM.2012.137" target="blank">
									10.1109/ASONAM.2012.137
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						 <br>Publication Year: 2012
							
										
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										The growing amount of data in healthcare social media requires innovative new analysis methods, which are elementary to exploration of relationship dynamics, community formation, sustenance <span class="snippet">and</span> dissolution, in a bid to understand the new roles social media play in healthcare. In this work we use network analysis to explore the temporal nature of two large diabetes social networks, with a view to enhancing our knowledge of the development of sub-community structures <span class="snippet">and</span> cohesion factors. Current results reveal how diabetes <span class="snippet">online</span> communities are very dynamic, suggesting diabetes patients are usually actively engaged for periods of less than a year, typically immediately following diagnosis. The presented empirical evidence inform future <span class="snippet">online</span> intervention strategies for promoting health behavior <span class="snippet">and</span> lifestyle changes among people with diabetes.
										
						                	
						                	
						                	
						                			
						             				
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										Discovering important nodes through comprehensive assessment theory on enron email database
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						Huijie Yang; Junyong Luo; Yan Liu; Meijuan Yin; Ding Cao 
					
					
						
						
							            
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			                	<br>Biomedical Engineering <span class="snippet">and</span> Informatics (BMEI), 2010 3rd International Conference on
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								<a href="http://dx.doi.org/10.1109/BMEI.2010.5639909" target="blank">
									10.1109/BMEI.2010.5639909
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						 <br>Publication Year: 2010
							
										
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									<p>
										One major problems in the field of <span class="snippet">social network analysis</span> is how to discover the important <span class="snippet">and</span> influential nodes based on the network structure. To address this challenge, we propose <span class="snippet">and</span> use some measures to measure node importance, such as degree measure, improved cluster coefficient measure <span class="snippet">and</span> a new ranking method based on reputation. Thinking of the unilateral influence of the single measure, we exploit a comprehensive assessment model to synthesize the three measures <span class="snippet">and</span> discover the interesting <span class="snippet">and</span> important nodes in the email communication network graph. The experimental results on Enron email dataset show our method is effective <span class="snippet">and</span> performs better on the problem of important nodes discovery than other measures.
										
						                	
						                	
						                	
						                			
						             				
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										Research on the Application of Expert Knowledge Map Based on <span class="snippet">Social Network Analysis</span>
									</a>
								
							
						
					
				</h3>
				
					
						Tong Liu; Yanqin Shi; Chensheng Wu 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5379503">
			                	<br>Dependable, Autonomic <span class="snippet">and</span> Secure Computing, 2009. DASC '09. Eighth IEEE International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/DASC.2009.70" target="blank">
									10.1109/DASC.2009.70
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										In this paper, the concept <span class="snippet">and</span> application of expert knowledge map are introduced, the new requirements to the application of expert knowledge map in the interdisciplinary innovation are summarized, then the basic theory of <span class="snippet">social network analysis</span> is manifested, then the application of the combination in the sci-tech innovation <span class="snippet">and</span> management is discussed. At last, a study on the relationship <span class="snippet">and</span> characters of the expert knowledge map is given in the SNA perspective <span class="snippet">and</span> analyzed by Ucinet.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5231875&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Prying Data out of a Social Network
									</a>
								
							
						
					
				</h3>
				
					
						Bonneau, J.; Anderson, J.; Danezis, G. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2009.45" target="blank">
									10.1109/ASONAM.2009.45
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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									 Cited by 190
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									<p>
										Preventing adversaries from compiling significant amounts of user data is a major challenge for social network operators. We examine the difficulty of collecting profile <span class="snippet">and</span> graph information from the popular social networking Website Facebook <span class="snippet">and</span> report two major findings. First, we describe several novel ways in which data can be extracted by third parties. Second, we demonstrate the efficiency of these methods on crawled data. Our findings highlight how the current protection of personal data is inconsistent with user's expectations of privacy.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6398191&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										CVPD: A tool based on a <span class="snippet">social network analysis</span> to combating viruses propagation
									</a>
								
							
						
					
				</h3>
				
					
						Mishra, S.; Agrawal, U.; Nandi, G.C. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6387505">
			                	<br>Communication, Information &amp; Computing Technology (ICCICT), 2012 International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
						   	;
						   
						
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966781">
								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ICCICT.2012.6398191" target="blank">
									10.1109/ICCICT.2012.6398191
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2012
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										It has been seen that <span class="snippet">Social network analysis</span> is gaining its applicability in several areas like business, marketing, biology, disease modeling, <span class="snippet">and</span> anti-terrorism. In this paper, we have discussed its practical application in the domain of computer network to identify distribution of computer viruses flowing through the network. To the best of our knowledge this is a novel idea <span class="snippet">and</span> is based on the gSpan (Graph based substructure Pattern Mining) algorithm for identifying frequent pattern of viruses flowing in a particular region of connected nodes. This crusades make analysist enabled to deal with the problems <span class="snippet">and</span> deploy more efficient antivirus in that region of nodes.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5730296&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Agile Knowledge Management System: Leveraging intra organizational Social Networking platforms for augmenting Knowledge Management system
									</a>
								
							
						
					
				</h3>
				
					
						Vadari, S.; Kummamuru, S. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5724309">
			                	<br>Business Applications of <span class="snippet">Social Network Analysis</span> (BASNA), 2010 IEEE International Workshop on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/BASNA.2010.5730296" target="blank">
									10.1109/BASNA.2010.5730296
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										This paper outlines how the various intra Social Networking platforms in the organization can be leveraged for establishing an Agile Knowledge Management (KM) System. The Authors have looked at the different KM <span class="snippet">and</span> Social Networking formats which are in vogue. They studied the effectiveness of the purpose for which these were instituted <span class="snippet">and</span> suggest ways for improving upon them to lead to better utilization of these Social Networking instruments paving way for Agile Knowledge Management in the organization in terms of contribution <span class="snippet">and</span> usage by employees. The success of this approach has been illustrated through a case of various Social Networking platforms launched in a large IT organization <span class="snippet">and</span> its effect on making its Knowledge Management nimble.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5541994&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Supporting the Requirements Prioritization Process Using <span class="snippet">Social Network Analysis</span> Techniques
									</a>
								
							
						
					
				</h3>
				
					
						Fitsilis, P.; Gerogiannis, V.; Anthopoulos, L.; Savvas, I.K. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5541619">
			                	<br>Enabling Technologies: Infrastructures for Collaborative Enterprises (WETICE), 2010 19th IEEE International Workshop on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/WETICE.2010.24" target="blank">
									10.1109/WETICE.2010.24
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
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										Requirements management <span class="snippet">and</span> prioritization is a complex process that should take into account requirements value for customers, cost of implementation, available resources, requirements interdependencies, system architecture <span class="snippet">and</span> dependencies to the code base. In this paper we present how <span class="snippet">Social Network Analysis</span> can be used in order to improve software requirements management <span class="snippet">and</span> the prioritization process. The presented model is based on meta-networks where basic entities are combined for representing requirements priorities, interdependencies, required knowledge, etc.. The analysis of the model is illustrated with sample data <span class="snippet">and</span> a number of examples.
										
						                	
						                	
						                	
						                			
						             				
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										Social Network Analyses based on e-portfolio assessment
									</a>
								
							
						
					
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						Jiong Ma; Xiao-zhong Chen 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5959270">
			                	<br>Computer Science <span class="snippet">and</span> Service System (CSSS), 2011 International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
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								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/CSSS.2011.5974579" target="blank">
									10.1109/CSSS.2011.5974579
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										As a quantitative analysis tool in the research field of Social Science. Social Network Analyses has been more <span class="snippet">and</span> more applied to the field of education these years. The thesis takes e-portfolio website as learning <span class="snippet">and</span> assessing platform Summed up the data of the learning <span class="snippet">and</span> Process Assessment, <span class="snippet">and</span> analyzed it by the angel of InDegree, OutDegree <span class="snippet">and</span> Betweenness, etc. Finally, compared the student's score which have the extreme value of Betweenness <span class="snippet">and</span> analyzed the relationship between student's score <span class="snippet">and</span> the value of Betweenness.
										
						                	
						                	
						                	
						                			
						             				
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										Identifying Social Communities by Frequent Pattern Mining
									</a>
								
							
						
					
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						Adnan, M.; Alhajj, R.; Rokne, J. 
					
					
						
						
							            
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			                	<br>Information Visualisation, 2009 13th International Conference
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/IV.2009.49" target="blank">
									10.1109/IV.2009.49
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						 <br>Publication Year: 2009
							
										
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										This paper presents a social network modeling technique that models the data to be analyzed to create a social network as frequent closed patterns. Frequent closed patterns have the advantage that they successfully grab the inherent information content of the dataset <span class="snippet">and</span> is applicable to a broader application domain. Entropies of the frequent closed patterns are used to keep the dimensionality of the feature vectors to a reasonable size. Experimental results presented in the paper shows that social network produced from these set of features successfully carries the community structure information.
										
						                	
						                	
						                	
						                			
						             				
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										Large-Scale <span class="snippet">Social Network Analysis</span> Based on MapReduce
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						Guojun Liu; Ming Zhang; Fei Yan 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5634609">
			                	<br>Computational Aspects of Social Networks (CASoN), 2010 International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/CASoN.2010.115" target="blank">
									10.1109/CASoN.2010.115
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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									<p>
										Social Networks Service (SNS), is becoming more <span class="snippet">and</span> more popular <span class="snippet">and</span> a lot of studies have been carried out in this active field. However, traditional analysis methods based on single machines is not suitable because the network is growing too large. MapReduce, a programming paradigm proposed by Google, gives us a new approach to solve large-scale social networks analysis problem by making use of the power of multi-machines. In this paper, we use Hadoop - an open source implementation of MapReduce - to conduct a series of analyses on large-scale social networks including several distributions, clustering coefficient <span class="snippet">and</span> diameter. We believe that our work will shed light on the study of measuring large-scale social networks.
										
						                	
						                	
						                	
						                			
						             				
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										A Case Study: Social Network <span class="snippet">and</span> Knowledge Sharing
									</a>
								
							
						
					
				</h3>
				
					
						Yanli Guo; Jianbin Chen 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5589107">
			                	<br>E-Business <span class="snippet">and</span> E-Government (ICEE), 2010 International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966917">
								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ICEE.2010.434" target="blank">
									10.1109/ICEE.2010.434
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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									<p>
										The purpose of this paper is to research the relationship between social network structure <span class="snippet">and</span> knowledge sharing in an organization or team, <span class="snippet">and</span> to analysis the impact on knowledge sharing form social network. Taking a project team for case, we interview all people of the team, <span class="snippet">and</span> draw a network diagram to discuss the relationship between social network <span class="snippet">and</span> knowledge sharing.
										
						                	
						                	
						                	
						                			
						             				
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				<h3>           
					
						
						
							
								
								
										
									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=4740434&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Social Tagging Behaviour in Community-Driven Question Answering
									</a>
								
							
						
					
				</h3>
				
					
						Rodrigues, E.M.; Milic-Frayling, N.; Fortuna, B. 
					
					
						
						
							            
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			                	<br>Web Intelligence <span class="snippet">and</span> Intelligent Agent Technology, 2008. WI-IAT '08. IEEE/WIC/ACM International Conference on
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								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/WIIAT.2008.138" target="blank">
									10.1109/WIIAT.2008.138
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2008
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										On-line community services such as Live QnA <span class="snippet">and</span> Yahoo! Answers enable their members to ask questions <span class="snippet">and</span> have them answered by the community. The questions are labelled by the users to facilitate search, navigation, <span class="snippet">and</span> recommendations. In this paper we provide an in-depth analysis of the question labelling practices by contrasting the use of community generated tags in the Live QnA service with the use of topic categories from a fixed taxonomy in the Yahoo! Answers service. We found that community tagging is related to higher levels of social interactions amongst users. Analysis of the most frequently used community tags reveals that active users may establish strong social ties around specific tags. Furthermore, the discriminative value of individual community tags can be low since the corresponding questions may cover a variety of topics. Thus, appropriate care needs to be taken when designing search, browsing, <span class="snippet">and</span> recommender features for question discovery.
										
						                	
						                	
						                	
						                			
						             				
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										A proposal to enhance cross-marketing via social networks by modelling <span class="snippet">and</span> analyzing market basket like patterns across social network groups
									</a>
								
							
						
					
				</h3>
				
					
						Bharath, C.; Ganesh, J. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5724309">
			                	<br>Business Applications of <span class="snippet">Social Network Analysis</span> (BASNA), 2010 IEEE International Workshop on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/BASNA.2010.5730301" target="blank">
									10.1109/BASNA.2010.5730301
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										The ability to create groups has for long been one of the features of interest for the users across the social networks. Groups allow users (account holders) to join them, be a fan-follower of them, etc. In addition, there have been provisions for the account holder to also be a part of multiple groups as a member. The primary goal of our research is to analyze patterns within group membership. Essentially, we will be proposing a model based on market basket analysis over a subset of the social network groups <span class="snippet">and</span> then derive a set of associative mining rules amongst these networks. The information mined may provide interesting insights for cross-marketing purposes. For instance, using this technique an organization O<sub>x</sub> could find that the buyers of their product x tend to be socially fans or members of groups with the subtype Y or even, perhaps, more specifically with another organization O<sub>y</sub> that sell a product y. Using such data, the organizations may then try to leverage these findings to build a co-opetative (Co-operation + Competition), profitable synergy <span class="snippet">and</span> might also give them insights to determine which products of O<sub>y</sub> may be impacted if the store discontinues selling the product x or vice-versa. The aim of this paper is to bring in a new high-level dimension to the <span class="snippet">social network analysis</span> area by imbibing the concepts of Market Basket <span class="snippet">and</span> then try to provide a basic feasibility criterion for the entire approach by simple simulations.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5551046&contentType=Journals+%26+Magazines&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										A Study of the Human Flesh Search Engine: Crowd-Powered Expansion of <span class="snippet">Online</span> Knowledge
									</a>
								
							
						
					
				</h3>
				
					
						Fei-Yue Wang; Zeng, D.; Hendler, J.A.; Qingpeng Zhang; Zhuo Feng; Yanqing Gao; Hui Wang; Guanpi Lai 
					
					
						
			              	               
			                <a href="http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=2">
			               		<br>Computer
			                </a>					
						
						
							
						
							
												
					
						
					
		       			<br>  
						Volume:	43					
					
						
					
						,<a href="http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=5551033">
							Issue: 8	
						</a>						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/MC.2010.216" target="blank">
									10.1109/MC.2010.216
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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									 Cited by 6
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										This first comprehensive empirical study of a search function that originated in China examines its tremendous growth in recent years <span class="snippet">and</span> its uniquely rich <span class="snippet">online</span>/offline interactions.
										
						                	
						                	
						                	
						                			
						             				
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										Character Identification in Feature-Length Films Using Global Face-Name Matching
									</a>
								
							
						
					
				</h3>
				
					
						Yi-Fan Zhang; Changsheng Xu; Hanqing Lu; Yeh-Min Huang 
					
					
						
			              	               
			                <a href="http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6046">
			               		<br>Multimedia, IEEE Transactions on
			                </a>					
						
						
							
						
							
												
					
						
					
		       			<br>  
						Volume:	11					
					
						
					
						,<a href="http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=5286795">
							Issue: 7	
						</a>						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294961108">
								Bioengineering</a>
						   
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								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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								Engineered Materials, Dielectrics &amp; Plasmas</a>
						   
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								Geoscience</a>
						   
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								Photonics &amp; Electro-Optics</a>
						   
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								Power, Energy, &amp; Industry Applications</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966781">
								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/TMM.2009.2030629" target="blank">
									10.1109/TMM.2009.2030629
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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							1276	- 1288
													
													
						
						
						 
						
					
					
					
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									 Cited by 7
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									<p>
										Identification of characters in films, although very intuitive to humans, still poses a significant challenge to computer methods. In this paper, we investigate the problem of identifying characters in feature-length films using video <span class="snippet">and</span> film script. Different from the state-of-the-art methods on naming faces in the videos, most of which used the local matching between a visible face <span class="snippet">and</span> one of the names extracted from the temporally local video transcript, we attempt to do a global matching between names <span class="snippet">and</span> clustered face tracks under the circumstances that there are not enough local name cues that can be found. The contributions of our work include: 1) A graph matching method is utilized to build face-name association between a face affinity network <span class="snippet">and</span> a name affinity network which are, respectively, derived from their own domains (video <span class="snippet">and</span> script). 2) An effective measure of face track distance is presented for face track clustering. 3) As an application, the relationship between characters is mined using <span class="snippet">social network analysis</span>. The proposed framework is able to create a new experience on character-centered film browsing. Experiments are conducted on ten feature-length films <span class="snippet">and</span> give encouraging results.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5974350&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Uncovering the informal organization applying <span class="snippet">social network analysis</span> in a corporate email system
									</a>
								
							
						
					
				</h3>
				
					
						Henriques, M.P.L.; MiraVentura, J.P. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5962051">
			                	<br>Information Systems <span class="snippet">and</span> Technologies (CISTI), 2011 6th Iberian Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										Today is widely accepted the importance of the unformal organization <span class="snippet">and</span> of the roles people play trough the relationships they establish outside of the formal organizational structure. In that sense a deeper knowledge of the unformal organization <span class="snippet">and</span> the roles played by the collaborators can be a valuable management asset. In this context <span class="snippet">social network analysis</span> is increansingly being seen as an important methodology to perform such assessment <span class="snippet">and</span>. The purpose of the present study is to identify patterns in the interactions between people in a corporate environment using a specific <span class="snippet">and</span> limited opportunity to sample real corporate data <span class="snippet">and</span> apply <span class="snippet">Social Network Analysis</span> techniques to a corporate email exchange history. Some interesting patterns emerged confirming the initial assumption of divergence between formal <span class="snippet">and</span> non-formal structures in corporate contexts.
										
						                	
						                	
						                	
						                			
						             				
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										Design <span class="snippet">and</span> implementation of SNODSOC: Novel class detection for <span class="snippet">social network analysis</span>
									</a>
								
							
						
					
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						Abrol, S.; Khan, L.; Khadilkar, V.; Thuraisingham, B.; Cadenhead, T. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6269208">
			                	<br>Intelligence <span class="snippet">and</span> Security Informatics (ISI), 2012 IEEE International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ISI.2012.6284312" target="blank">
									10.1109/ISI.2012.6284312
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						 <br>Publication Year: 2012
							
										
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										This paper describes a framework, SNODSOC (Stream based novel class detection for <span class="snippet">social network analysis</span>), that detects evolving patterns <span class="snippet">and</span> trends in social microblogs. SNODSOC extends our powerful data mining system, SNOD (Stream-based Novel Class Detection) for now detecting novel patterns <span class="snippet">and</span> trends within microblogs.
										
						                	
						                	
						                	
						                			
						             				
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										From Dark Web to GeoPolitical Web: Exploring the Value of Social Media Informatics
									</a>
								
							
						
					
				</h3>
				
					
						Hsinchun Chen 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6298753">
			                	<br>Intelligence <span class="snippet">and</span> Security Informatics Conference (EISIC), 2012 European
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/EISIC.2012.70" target="blank">
									10.1109/EISIC.2012.70
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						 <br>Publication Year: 2012
							
										
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										Summary form only given. This talk will summarize the University of Arizona Artificial Intelligence Lab's Dark Web research <span class="snippet">and</span> discuss current <span class="snippet">and</span> future development of a new GeoPolitical Web research program, which aims to study the emerging geopolitical events using advanced social media analytics techniques. The GeoPolital Web project is collecting longitudinal multilingual social media contents (forums, twitters, videos) from many different volatile regions in the world. Selected sentiment analysis, text mining, <span class="snippet">social network analysis</span>, <span class="snippet">and</span> econometrics models studies will be presented. Dark Web research has received significant international press coverage, including: Associated Press, USA Today, The Economist, NSF Press, Washington Post, Fox News, BBC, PBS, Business Week, Discover magazine, WIRED magazine, Government Computing Week, Second German TV (ZDF), Toronto Star, <span class="snippet">and</span> Arizona Daily Star, among others. For more Dark Web <span class="snippet">and</span> GeoPolitical Web project information, please see: http://ai.eller.arizona.edu/research/terror/.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6137336&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Cross-Temporal Link Prediction
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						Oyama, S.; Hayashi, K.; Kashima, H. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6135855">
			                	<br>Data Mining (ICDM), 2011 IEEE 11th International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/ICDM.2011.45" target="blank">
									10.1109/ICDM.2011.45
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						 <br>Publication Year: 2011
							
										
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										The increasing interest in dynamically changing networks has led to growing interest in a more general link prediction problem called temporal link prediction in the data mining <span class="snippet">and</span> machine learning communities. However, only links in identical time frames are considered in temporal link prediction. We propose a new link prediction problem called cross-temporal link prediction in which the links among nodes in different time frames are inferred. A typical example of cross-temporal link prediction is cross-temporal entity resolution to determine the identity of real entities represented by data objects observed in different time periods. In dynamic environments, the features of data change over time, making it difficult to identify cross-temporal links by directly comparing observed data. Other examples of cross-temporal links are asynchronous communications in social networks such as Face book <span class="snippet">and</span> Twitter, where a message is posted in reply to a previous message. We adopt a dimension reduction approach to cross-temporal link prediction, that is, data objects in different time frames are mapped into a common low-dimensional latent feature space, <span class="snippet">and</span> the links are identified on the basis of the distance between the data objects. The proposed method uses different low-dimensional feature projections in different time frames, enabling it to adapt to changes in the latent features over time. Using multi-task learning, it jointly learns a set of feature projection matrices from the training data, given the assumption of temporal smoothness of the projections. The optimal solutions are obtained by solving a single generalized eigenvalue problem. Experiments using a real-world set of bibliographic data for cross-temporal entity resolution showed that introducing time-dependent feature projections improves the accuracy of link prediction.
										
						                	
						                	
						                	
						                			
						             				
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										Measuring User Prestige <span class="snippet">and</span> Interaction Preference on Social Network Site
									</a>
								
							
						
					
				</h3>
				
					
						Lailei Huang; Zhengyou Xia 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5222859">
			                	<br>Computer <span class="snippet">and</span> Information Science, 2009. ICIS 2009. Eighth IEEE/ACIS International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/ICIS.2009.106" target="blank">
									10.1109/ICIS.2009.106
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						 <br>Publication Year: 2009
							
										
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										Measuring user's prestige <span class="snippet">and</span> interaction preference is an important issue for social network site implementer. To address this problem, an empirical study based on xiaonei.com is conducted. In this work, we introduce the "interaction prestige" metrics based on the observation of user's interaction features. Users with distinct levels of "interaction prestige" are grouped into different categories by a clustering algorithm <span class="snippet">and</span> named as "outgoing", "reciprocal" <span class="snippet">and</span> "incoming" type respectively. We demonstrate that the "interaction prestige" of users is related with other prestige metrics including "virtual prestige", "social prestige" <span class="snippet">and</span> "structural prestige". Further more, the chi-square test for independence shows that there exist significant communication patterns among the identified types of users. We observe that users tend to choose those with high interaction prestige <span class="snippet">and</span> opposite gender as their communication partner.
										
						                	
						                	
						                	
						                			
						             				
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										The Application of SNA in IT-Based BRP
									</a>
								
							
						
					
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						Min Zhu; Weidong Liu; Wensong Hu; Zhuo Fang 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5367971">
			                	<br>Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on
			                </a>
						
							
						
							
												
					
						
					
		       			<br>  
						Volume:	3					
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/IITA.2009.329" target="blank">
									10.1109/IITA.2009.329
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										SNA is an established social science approach of studying human relations ad social structures. The goal of the paper is to demonstrate how the social network paradigm supports the task of designing information technology based business processes in company A by discussing the analysis procedures in detail <span class="snippet">and</span> drawing a conclusion by the comparation.
										
						                	
						                	
						                	
						                			
						             				
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										<span class="snippet">Social Network Analysis</span> in Multiple Social Networks Data for Criminal Group Discovery
									</a>
								
							
						
					
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						Xufeng Shang; Yubo Yuan 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6383189">
			                	<br>Cyber-Enabled Distributed Computing <span class="snippet">and</span> Knowledge Discovery (CyberC), 2012 International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967046">
								Components, Circuits, Devices &amp; Systems</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/CyberC.2012.14" target="blank">
									10.1109/CyberC.2012.14
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2012
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										A criminal network is a kind of social networks with both secrecy <span class="snippet">and</span> efficiency. The hidden knowledge in criminal networks can be regarded as an important indicators for criminal investigations which can help finding the criminal's relationship <span class="snippet">and</span> identifying suspects. However such criminal network analysis has not been studied well in an applied way <span class="snippet">and</span> remains primarily a manual process. To assist investigators to find criminals relationship, criminal leader, <span class="snippet">and</span> identify suspicious guys of a conspiracy, we built a comprehensive indicator model using methods developed in the field of <span class="snippet">Social Network Analysis</span> (SNA). A simulation is done on a large office from open source reports <span class="snippet">and</span> the ranked list with respect to comprehensive indicator indicates that we provide a reasonable ranking based on the proposed model.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6228426&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Traceable Content Distribution Using Wavelet Decomposition <span class="snippet">and</span> <span class="snippet">Social Network Analysis</span>
									</a>
								
							
						
					
				</h3>
				
					
						Conghuan Ye; Ji Li; Zenggang Xiong 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6226980">
			                	<br>Computer, Consumer <span class="snippet">and</span> Control (IS3C), 2012 International Symposium on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/IS3C.2012.203" target="blank">
									10.1109/IS3C.2012.203
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2012
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										This paper identifies a novel secure content distribution framework based on wavelet decomposition <span class="snippet">and</span> social network. The motivation is to facilitate trace illegally distributed content by using mapping between hierarchical community of social network <span class="snippet">and</span> wavelet tree. Firstly, the host signal is encrypted, then the encrypted contents are delivered to users according to <span class="snippet">social network analysis</span>, at last, the contents are fingerprinted with decryption on the receiving side to get many different copies. Fingerprints which are constructed with <span class="snippet">social network analysis</span> are embedded in wavelet domain. The use of fingerprinting along with encryption can provide a double-layer of protection to digital media. Theory analysis proves that the proposed framework has significantly better performance than traditional content distribution mode. Experimental results prove that the proposed technique is not only resilient to collusion attacks, but also consume much less CPU time to trace colluders.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6413904&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Link Prediction <span class="snippet">and</span> Recommendation across Heterogeneous Social Networks
									</a>
								
							
						
					
				</h3>
				
					
						Yuxiao Dong; Jie Tang; Sen Wu; Jilei Tian; Chawla, N.V.; Jinghai Rao; Huanhuan Cao 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6412852">
			                	<br>Data Mining (ICDM), 2012 IEEE 12th International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ICDM.2012.140" target="blank">
									10.1109/ICDM.2012.140
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2012
							
										
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									<p>
										Link prediction <span class="snippet">and</span> recommendation is a fundamental problem in <span class="snippet">social network analysis</span>. The key challenge of link prediction comes from the sparsity of networks due to the strong disproportion of links that they have potential to form to links that do form. Most previous work tries to solve the problem in single network, few research focus on capturing the general principles of link formation across heterogeneous networks. In this work, we give a formal definition of link recommendation across heterogeneous networks. Then we propose a ranking factor graph model (RFG) for predicting links in social networks, which effectively improves the predictive performance. Motivated by the intuition that people make friends in different networks with similar principles, we find several social patterns that are general across heterogeneous networks. With the general social patterns, we develop a transfer-based RFG model that combines them with network structure information. This model provides us insight into fundamental principles that drive the link formation <span class="snippet">and</span> network evolution. Finally, we verify the predictive performance of the presented transfer model on 12 pairs of transfer cases. Our experimental results demonstrate that the transfer of general social patterns indeed help the prediction of links.
										
						                	
						                	
						                	
						                			
						             				
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										SONAR: Towards User-centric <span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Visualization
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						Hutter, C.; Hartmann, B.; Bohm, K.; Heistermann, T.; Kohlmeyer, K.-S.; Reckling, R.; Reiche, M.; Parra, D.S. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5613987">
			                	<br>Web Intelligence <span class="snippet">and</span> Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on
			                </a>
						
							
						
							
												
					
						
					
		       			<br>  
						Volume:	1					
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/WI-IAT.2010.67" target="blank">
									10.1109/WI-IAT.2010.67
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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										<span class="snippet">Social network analysis</span> (SNA) has attracted a lot of attention over the past years. Existing tools for SNA do not allow a user-centric analysis of the social neighborhood, i.e., the subgraph of the user's friends <span class="snippet">and</span> friends of a friend. In this paper, we introduce SONAR, an open source Web application for user-centric SNA. Its extensible architecture <span class="snippet">and</span> flexible data model allows developers to embed SONAR directly into social networking websites. A performance evaluation shows that our application scales well with the number of users <span class="snippet">and</span> adds only minimal overhead to the SNA algorithms.
										
						                	
						                	
						                	
						                			
						             				
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										Finding <span class="snippet">and</span> Analyzing Social Collaboration Networks in the Mexican Computer Science Community
									</a>
								
							
						
					
				</h3>
				
					
						Garcia-Bauelos, L.; Portilla, A.; Chavez-Aragon, A.; Reyes-Galaviz, O.F.; Ayanegui-Santiago, H. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5452232">
			                	<br>Computer Science (ENC), 2009 Mexican International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ENC.2009.17" target="blank">
									10.1109/ENC.2009.17
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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										Collaboration of peers is rather common in some scientific communities <span class="snippet">and</span> is being facilitated with the advances in telecommunication <span class="snippet">and</span> computer networking technologies. In this paper, we analyze the collaboration networks formed among Mexican computer science scholars, using <span class="snippet">social network analysis</span> techniques. A series of measurements are performed to identify some patterns of collaboration both among individuals <span class="snippet">and</span> among Mexican academic institutions. The data for our measurements was taken from two freely available sources: DBLP, a public digital library which indexes computer science related conferences <span class="snippet">and</span> journals; <span class="snippet">and</span> the census of Mexican scholars made by REMIDEC. In order to have an insight about the impact of publications, we used the CORE ranking for computer science conferences <span class="snippet">and</span> journals. Our aim is to gain a better understanding of the working practices exhibited by the Mexican computer science community.
										
						                	
						                	
						                	
						                			
						             				
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			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2011 International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
						   	;
						   
						
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2011.10" target="blank">
									10.1109/ASONAM.2011.10
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										Predicting User Behaviour to Facilitate Efficient Provision of Health Applications
									</a>
								
							
						
					
				</h3>
				
					
						El-Hajj, M.; Hayward, R.S. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2009.51" target="blank">
									10.1109/ASONAM.2009.51
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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										Practical analysis of user behavior patterns in social network <span class="snippet">and</span> community software is one of the many application of data mining tools. Using data mining techniques, over 100 users of the Vividesk private <span class="snippet">online</span> network ('desktop') for Canadian healthcare professionals was examined over a four year period for user behavior trends using decision trees (DTs) mining. Vividesk provides users with an <span class="snippet">online</span> community of research <span class="snippet">and</span> of practice, enabling clients to use Web 2.0 <span class="snippet">and</span> social networking principles to enhance their medical research <span class="snippet">and</span> practice. Our interest rests primarily in usage patterns related to usergroups <span class="snippet">and</span> classes of applications on these desktops. Some applications link to licensed information resources which can cost thousands of dollars per year to license. As a result, examining application usage data can help generate information on the relative cost effectiveness of the resources for which clients pay. This study presents an initial analysis of data by grouped resource applications as a means of determining whether deeper analysis is warranted. Data was warehoused <span class="snippet">and</span> mined using a Microsoft Business intelligence tool (cube), using predetermined dimensions. By warehousing <span class="snippet">and</span> mining desktop application usage through DTs, previously hidden usage patterns were uncovered. The DT experiments revealed various application usage patterns both within <span class="snippet">and</span> between the desktops. Usergroups within each environment also demonstrated different access patterns at different times. Based on this analysis, further drilling down is warranted to uncover patterns of particular resource use. Exercises such as these will help predict future user behavior <span class="snippet">and</span> facilitate the planning of desktop resource provision.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6132774&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										An Exploratory <span class="snippet">Social Network Analysis</span> of Academic Research Networks
									</a>
								
							
						
					
				</h3>
				
					
						Toral, S.L.; Bessis, N.; Martinez-Torres, M.R.; Franc, F.; Barrero, F.; Xhafa, F. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6132214">
			                	<br>Intelligent Networking <span class="snippet">and</span> Collaborative Systems (INCoS), 2011 Third International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/INCoS.2011.49" target="blank">
									10.1109/INCoS.2011.49
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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									 Cited by 3
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
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										For several decades, academics around the world have been collaborating with the view to support the development of their research domain. Having said that, the majority of scientific <span class="snippet">and</span> technological policies try to encourage the creation of strong inter-related research groups in order to improve the efficiency of research outcomes <span class="snippet">and</span> subsequently research funding allocation. In this paper, we attempt to highlight <span class="snippet">and</span> thus, to demonstrate how these collaborative networks are developing in practice. To achieve this, we have developed an automated tool for extracting data about joint article publications <span class="snippet">and</span> analyzing them from the perspective of <span class="snippet">social network analysis</span>. In this case study, we have limited data from works published in 2010 by England academic <span class="snippet">and</span> research institutions. The outcomes of this work can help policy makers in realising the current status of research collaborative networks in England.
										
						                	
						                	
						                	
						                			
						             				
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										Research on emergency information management based on the <span class="snippet">social network analysis</span>
									</a>
								
							
						
					
				</h3>
				
					
						Wen Lin; Kang Wei; Gu Jie-ying 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6177336">
			                	<br>Information Systems for Crisis Response <span class="snippet">and</span> Management (ISCRAM), 2011 International Conference on
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								Aerospace</a>
						   
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								Bioengineering</a>
						   
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								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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								Fields, Waves &amp; Electromagnetics</a>
						   
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								Power, Energy, &amp; Industry Applications</a>
						   
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								Signal Processing &amp; Analysis</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967025">
								Transportation</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ISCRAM.2011.6184103" target="blank">
									10.1109/ISCRAM.2011.6184103
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										In recent years, the emergency information management in cities has been one of the hot issues of study in the field of public emergency management. Based on the <span class="snippet">social network analysis</span>, this article makes a study of the incident of the water pollution in Xin'anjiang River, an city emergency in china in 2011. Setting up diagraphs of topology with the help of Ucinet software, the author calculates the density of network <span class="snippet">and</span> the average distance in the whole network structure <span class="snippet">and</span> also calculates point centrality, betweenness centrality, closeness centrality, etc. in the analysis of centrality. The author tries to seek effective ways of management on emergency information by using the analysis of this typical case as a breakthrough in study, in hopes of achieving the effective information management <span class="snippet">and</span> control by multiple main bodies in the social network under complex circumstances <span class="snippet">and</span> providing support for the government to make decisions in dealing with emergencies.
										
						                	
						                	
						                	
						                			
						             				
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										Analyzing conversations with dynamic graph visualization
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						Pupyrev, S.; Tikhonov, A. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5681599">
			                	<br>Intelligent Systems Design <span class="snippet">and</span> Applications (ISDA), 2010 10th International Conference on
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								<a href="http://dx.doi.org/10.1109/ISDA.2010.5687175" target="blank">
									10.1109/ISDA.2010.5687175
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						 <br>Publication Year: 2010
							
										
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										In this paper, we consider the problem of analysis <span class="snippet">and</span> visualization of <span class="snippet">online</span> conversations (chat histories, email archives, etc.). We present a dynamic graph drawing algorithm based on modification of multidimensional scaling. The algorithm builds a layout of sequence of graphs <span class="snippet">and</span> produces a slice view of the evolution of <span class="snippet">online</span> communications. The method have been applied for visualization of two real-world datasets. We show how to use these visualizations for analyzing <span class="snippet">and</span> extracting hidden temporal patterns from <span class="snippet">online</span> conversation data.
										
						                	
						                	
						                	
						                			
						             				
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										Managing knowledge transfer across supply chain: A social network approach
									</a>
								
							
						
					
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						Marra, M.; Ho, W.; Edwards, J. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5676675">
			                	<br>Supply Chain Management <span class="snippet">and</span> Information Systems (SCMIS), 2010 8th International Conference on
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						 <br>Publication Year: 2010
							
										
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										This paper analyzes the theme of knowledge transfer in supply chain management. The aim of this study is to present the <span class="snippet">social network analysis</span> (SNA) as an useful tool to study knowledge networks within supply chain, to monitor knowledge flows <span class="snippet">and</span> to identify the accumulating knowledge nodes of the networks.
										
						                	
						                	
						                	
						                			
						             				
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										Granular computing for Intelligent Social Network modeling <span class="snippet">and</span> cooperative decisions
									</a>
								
							
						
					
				</h3>
				
					
						Yager, R.R. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4662488">
			                	<br>Intelligent Systems, 2008. IS '08. 4th International IEEE Conference
			                </a>
						
							
						
							
												
					
						
					
		       			<br>  
						Volume:	1					
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/IS.2008.4670392" target="blank">
									10.1109/IS.2008.4670392
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						 <br>Publication Year: 2008
							
										
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									 Cited by 1
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									<p>
										Our objective here is to take a step toward the development of intelligent <span class="snippet">social network analysis</span> using granular computing. In particular one can start by expressing in a human focused manner concepts associated with social networks then formalize these concepts using fuzzy sets <span class="snippet">and</span> then evaluate these concepts with respect to social networks which have been represented using set based relational network theory. We capture this approach in what we call the paradigm for intelligent <span class="snippet">social network analysis</span>, ISNA. Using this paradigm we provide definitions of a number of concepts related to social networks.
										
						                	
						                	
						                	
						                			
						             				
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				<h3>           
					
						
						
							
								
								
										
									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5231810&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Ranking Learning on the Web by Integrating Network-Based Features
									</a>
								
							
						
					
				</h3>
				
					
						Yingzi Jin; Matsuo, Y.; Ishizuka, M. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/ASONAM.2009.39" target="blank">
									10.1109/ASONAM.2009.39
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
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										Many efforts are undertaken by people <span class="snippet">and</span> companies to improve their popularity, growth, <span class="snippet">and</span> power, the outcomes of which are all expressed as rankings (designated as target rankings). Are these rankings merely the results of its elements' own attributes? In the theory of <span class="snippet">social network analysis</span> (SNA), the performance <span class="snippet">and</span> power of actors are usually interpreted as relations <span class="snippet">and</span> the relational structures they embedded. In this study, we propose an algorithm to generate <span class="snippet">and</span> integrate network-based features systematically from a given social network that mined from the Web to learn a model for explaining target rankings. Experimental results for learning to rank researchers' productivity based on social networks confirm the effectiveness of our models. This paper specifically examines the application of a social network that provides an example of advanced utilization of social networks mined from the Web.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6406288&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Parallelizing Preferential Attachment Models for Generating Large-Scale Social Networks that Cannot Fit into Memory
									</a>
								
							
						
					
				</h3>
				
					
						Yi-Chen Lo; Cheng-Te Li; Shou-De Lin 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6403618">
			                	<br>Privacy, Security, Risk <span class="snippet">and</span> Trust (PASSAT), 2012 International Conference on <span class="snippet">and</span> 2012 International Confernece on Social Computing (SocialCom)
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
						   	;
						   
						
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967046">
								Components, Circuits, Devices &amp; Systems</a>
						   
						   	;
						   
						
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/SocialCom-PASSAT.2012.28" target="blank">
									10.1109/SocialCom-PASSAT.2012.28
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2012
							
										
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									<p>
										Social network generation is an important problem in <span class="snippet">social network analysis</span>. The goal is to produce artificial networks that preserve some real world properties of social networks. As one of most popular social network generation algorithms, the Barabási -- Albert (BA) model is a method that can generate random social networks with power-law degree distribution. This paper discusses the situation of generating large-sized social network that cannot fit into the memory. We design a parallel framework to tackle this problem. The challenge lies in the fact that the preferential attachment mechanism used in the BA model has direct conflict with the concept of parallelism. To achieve the preferential attachment, during the generation processes the degree information of nodes needs to be known, which prohibits the parallelism that allows nodes to generate edges independently. To handle this issue, this paper proposes a method to generate the expected accumulated degree of vertices for the parallel BA model. We further propose several novel techniques to reduce the complexity of generating N vertices with P processes to O(NlogN/P). We implement the model using MapReduce <span class="snippet">and</span> the experiment results show that our model can produce billion-sized scale-free networks in minutes.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5730294&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Randomized tag recommendation in social networks <span class="snippet">and</span> classification of spam posts
									</a>
								
							
						
					
				</h3>
				
					
						Ravindran, P.P.; Mishra, A.; Kesavan, P.; Mohanavalli, S. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5724309">
			                	<br>Business Applications of <span class="snippet">Social Network Analysis</span> (BASNA), 2010 IEEE International Workshop on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/BASNA.2010.5730294" target="blank">
									10.1109/BASNA.2010.5730294
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						 <br>Publication Year: 2010
							
										
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										Tag recommendation is an integral part of any bookmarking application. With the growing popularity in Web 2.0 usage, recommending tags is of utmost importance in enabling a user to perform bookmarking easily. An issue that most recommendation systems do not consider is that users have a tendency to choose from tags that are suggested to them, which might bias the true popular rankings of tags. In this paper we consider the problem of tag recommendation for bookmarks based on user feedback. We propose an approach for automatic tag recommendation by using a suggestion set <span class="snippet">and</span> discuss a randomized suggestion rule for learning the true popular tags. As our algorithm depends on the frequency of tag suggestions, the action of spammers <span class="snippet">and</span> malicious users may result in skewed ranking of tags for a bookmark. Hence, there arises a need to identify malicious users <span class="snippet">and</span> spam posts to reinforce the efficiency of our algorithm because spammers can easily mislead a system. We have proposed an approach for classifying spammers based on tagging history of users. Our approach basically estimates the probability of a user being a spammer by analyzing previous posts <span class="snippet">and</span> tags. Our analysis on a dataset of a popular bookmarking site shows that the proposed method is effective in suggesting the true popular tags <span class="snippet">and</span> identifying spammers.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5300270&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Peer-to-peer unstructured anycasting using correlated swarms
									</a>
								
							
						
					
				</h3>
				
					
						Patankar, P.; Nam, G.; Kesidis, G.; Konstantopoulos, T.; Das, C.R. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5290608">
			                	<br>Teletraffic Congress, 2009. ITC 21 2009. 21st International
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966781">
								Signal Processing &amp; Analysis</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967025">
								Transportation</a>
						   
												
					 
																
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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									 Cited by 1
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										Over the recent years, <span class="snippet">social network analysis</span> has received renewed interest because of the significant increase in the number of users relying on applications based on them. An important criterion for the success of any social-networking based application is the efficiency of search. In this paper, we propose <span class="snippet">and</span> analyze a method of anycast search based on correlated communities or subgroups, i.e., using group-to-group caching. It works by restricting search to peers that belong to communities which are highly correlated with the requested community. We analytically prove that our proposed method works better than basic random walk, which remains a widely used method for performing search in these networks. Indeed our experiments prove that the proposed method reduces the search time by as much as 30% to that based on random walk. Our experiments also indicate that the proposed method outperforms basic random walk even under considerable peer-churn.
										
						                	
						                	
						                	
						                			
						             				
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										Fitting Network Data Based on Latent Cluster Model
									</a>
								
							
						
					
				</h3>
				
					
						Ying Guo; Xuefeng Wang; Donghua Zhu; Xiao Zhou 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5996071">
			                	<br>Management <span class="snippet">and</span> Service Science (MASS), 2011 International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966917">
								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ICMSS.2011.5999182" target="blank">
									10.1109/ICMSS.2011.5999182
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										In the last ten years, <span class="snippet">social network analysis</span> became a very popular topic in many different scientific fields, network models are also widely popular for representing the relationship of the network data. Network data exhibits transitivity <span class="snippet">and</span> homophily of the actors. There exist many distance computation methods for the actors space distance, <span class="snippet">and</span> two of them are the most famous for the latent position cluster model, here we used the latent cluster model which focus on clusters of actors or ties. In this paper, we compared two distance definition methods with different latent position cluster method, the two-stage method with Euclidean distance(TMED) model <span class="snippet">and</span> the bayesian estimation method with Bilinear latent(BEBL) model. The model make simulate the network dataset easy, <span class="snippet">and</span> compared the mcmc diagnostics.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=4812553&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										SmallBlue: <span class="snippet">Social Network Analysis</span> for Expertise Search <span class="snippet">and</span> Collective Intelligence
									</a>
								
							
						
					
				</h3>
				
					
						Ching-Yung Lin; Nan Cao; Shi Xia Liu; Papadimitriou, S.; Jimeng Sun; Xifeng Yan 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4812371">
			                	<br>Data Engineering, 2009. ICDE '09. IEEE 25th International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ICDE.2009.140" target="blank">
									10.1109/ICDE.2009.140
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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									 Cited by 4
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										SmallBlue is a social networking application that unlocks the valuable business intelligence of 'who knows what?', 'who knows whom?' <span class="snippet">and</span> 'who knows what about whom' within an organization, without requiring explicit involvement of individuals. The aim of SmallBlue is to locate knowledgeable colleagues, communities, <span class="snippet">and</span> knowledge networks in companies. The suite also helps users manage their personal networks, <span class="snippet">and</span> reach out to their extended network (the friends of their friends) to find <span class="snippet">and</span> access expertise <span class="snippet">and</span> information.
										
						                	
						                	
						                	
						                			
						             				
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										Comparison of Feature-Based Criminal Network Detection Models with k-Core <span class="snippet">and</span> n-Clique
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						Ozgul, F.; Erdem, Z.; Bowerman, C.; Atzenbeck, C. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5562738">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2010 International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2010.45" target="blank">
									10.1109/ASONAM.2010.45
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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										Four group detection models (e.g. GDM, OGDM, SoDM <span class="snippet">and</span> ComDM) are developed based on crime data features. These detection models are compared more common baseline SNA group detection algorithms. It is intended to find out, whether these four crime data specific group detection models can perform better than widely used k-core <span class="snippet">and</span> n-clique algorithms. Two data sets which contain previously known criminal networks are used as testbeds.
										
						                	
						                	
						                	
						                			
						             				
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										The Effect of Network Position <span class="snippet">and</span> Relationship Quality on Organizational Performance--The ABW Family
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						Ting-Lin Lee; Chih-Wei Lin 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5282954">
			                	<br>Computational Science <span class="snippet">and</span> Engineering, 2009. CSE '09. International Conference on
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		       			<br>  
						Volume:	4					
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/CSE.2009.90" target="blank">
									10.1109/CSE.2009.90
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						 <br>Publication Year: 2009
							
										
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										The interactions among firms varied by their network positions they were influenced. Our concern is to consider the relationship among network position, relationship quality, organizational learning <span class="snippet">and</span> organizational performance. This study analyzes the ABW Family, say Acer group, BenQ group <span class="snippet">and</span> Wistron group, from a social network aspect. Therefore, this paper is intended as an investigation of ABW Family, <span class="snippet">and</span> explore if interaction learning among firms of ABW Family lead to performance improvement. Results show the firms among ABW Family indeed closely <span class="snippet">and</span> actively interact, the relationship quality influence organizational performance the most; <span class="snippet">and</span> the ldquodegreerdquo is the most interpretable position variable for organizational performance.
										
						                	
						                	
						                	
						                			
						             				
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										The Use of <span class="snippet">Social Network Analysis</span> in Knowledge Diffusion Research from Patent Data
									</a>
								
							
						
					
				</h3>
				
					
						Chao-Chih Hsueh; Chun-Chieh Wang 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2009.68" target="blank">
									10.1109/ASONAM.2009.68
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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									<p>
										Patent documents are an ample source of technical <span class="snippet">and</span> commercial knowledge <span class="snippet">and</span>, thus, patent analysis has long been considered a useful vehicle for R&amp;D management <span class="snippet">and</span> technoeconomic analysis. In terms of techniques for patent analysis, citation analysis has been the most frequently adopted tool. In this research, we note that citation analysis is subject to some crucial drawbacks <span class="snippet">and</span> propose a network-based analysis, an alternative method for citation analysis. By using an illustrative data set, the overall process of developing patent network is described. The main objective is to understand that the knowledge diffuse between institution <span class="snippet">and</span> countries. The knowledge diffusion networks are described by using critical node, core network, <span class="snippet">and</span> network measures analysis .This includes identifying key influential players, the knowledge spillover patterns among them, <span class="snippet">and</span> the overall knowledge spillover efficiency. The proposed framework is applied to the field of liquid crystal display (LCD), including the patent citation networks of assignee <span class="snippet">and</span> countries. The analysis shows that the Japan is the most important citation center in LCD research <span class="snippet">and</span> the LG Phillps LCD, Sharp Samsung, Hitachi, IBM, <span class="snippet">and</span> AUO are central points of knowledge flow.
										
						                	
						                	
						                	
						                			
						             				
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										Simple Blog Searching framework based on <span class="snippet">social network analysis</span>
									</a>
								
							
						
					
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						Dolińska, I. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5676244">
			                	<br>Computer Science <span class="snippet">and</span> Information Technology (IMCSIT), Proceedings of the 2010 International Multiconference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967026">
								Aerospace</a>
						   
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								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										Blogs are very popular Internet communication tools. The process of knowledge sharing is a very important activity in the contemporary information era. Blogs are used for knowledge sharing on any subject all over the world. Knowledge gathered on blogs can be used in personal e-learning, which is a more informal <span class="snippet">and</span> personal way of learning than the one offered by traditional e-learning courses. However, it is not easy to find valuable knowledge in the huge amount of invalid information. In this study the Simple Blog Searching framework is proposed to improve the blog searching process. The <span class="snippet">social network analysis</span> methods of centrality measuring help to choose more easily the best results form the long list of hits, received from a blog search tool. To incorporate <span class="snippet">social network analysis</span> methods, the blog searching have to be expanded with the blog links searching.
										
						                	
						                	
						                	
						                			
						             				
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										From Social Networks to Behavioral Networks in Recommender Systems
									</a>
								
							
						
					
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						Esslimani, I.; Brun, A.; Boyer, A. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2009.30" target="blank">
									10.1109/ASONAM.2009.30
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						 <br>Publication Year: 2009
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
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										Recommender systems are widely used for personalization of information on the web <span class="snippet">and</span> information retrieval systems. Collaborative Filtering (CF) is the most popular recommendation technique. However, classical CF systems use only direct links <span class="snippet">and</span> common features to model relationships between users. This paper presents a new Collaborative Filtering approach (BNCF) based on a behavioral network that uses navigational patterns to model relationships between users <span class="snippet">and</span> exploits social networks techniques, such as transitivity, to explore additional links throughout the behavioral network. The final aim consists in involving these new links in prediction generation, to improve recommendations quality. BNCF is evaluated in terms of accuracy on a real usage dataset. The experimentation shows the benefit of exploiting new links to compute predictions. Indeed, BNCF highly improves the accuracy of predictions, especially in terms of HMAE.
										
						                	
						                	
						                	
						                			
						             				
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										Hybrid elicitation of latent intent in open societies (HELIOS)
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						Palmer, J. 
					
					
						
						
							            
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			                	<br>Cybersecurity Summit (WCS), 2011 Second Worldwide
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						 <br>Publication Year: 2011
							
										
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										The reach of ubiquitous social networking tools enabled through new technology is evident in the recent regime change <span class="snippet">and</span> the increased unrest within the Middle East. Such rapid dissemination of information may engender an equally rapid emergence of virtual communities of interest - some with potentially hostile intent. Twitter's accessibility <span class="snippet">and</span> popularity have attracted a large number of automated programs, known as bots, which can serve as a double-edged sword within Twitter. While legitimate bots generate a large amount of benign tweets delivering news <span class="snippet">and</span> updating information, malicious bots spread misinformation with potentially virulent results. This paper blends methods from traditional statistical classification methods with latent Dirichlet allocation with techniques of <span class="snippet">social network analysis</span> to underpin threat assessment based upon similarities discerned among topic sets that have distilled by semantic analysis of Twitter intercepts.
										
						                	
						                	
						                	
						                			
						             				
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										Definition <span class="snippet">and</span> Measures of an Opinion Model for Mining Forums
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						Stavrianou, A.; Velcin, J.; Chauchat, J.-H. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2009.34" target="blank">
									10.1109/ASONAM.2009.34
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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										<span class="snippet">Online</span> discussion systems in the form of forums have recently been analyzed by using graphs <span class="snippet">and</span> social network techniques. Each forum is regarded as a social network <span class="snippet">and</span> it is modeled by a graph whose vertices represent forum participants. In this paper, we focus on the structure <span class="snippet">and</span> the opinion content of the forum posts <span class="snippet">and</span> we are looking at the social network that is developed from a semantics point of view. We formally define an opinion-oriented model whose purpose is to provide complementary information to the knowledge extracted by the social network model. We define <span class="snippet">and</span> present measures that can give important information regarding the opinion flow as well as the general attitude of users <span class="snippet">and</span> towards users throughout the whole forum. Applying our model to a real forum found on the Web shows the additional information that can be extracted.
										
						                	
						                	
						                	
						                			
						             				
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										Dynamics of Inter-Meeting Time in Human Contact Networks
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						Yoneki, E.; Greenfield, D.; Crowcroft, J. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/ASONAM.2009.42" target="blank">
									10.1109/ASONAM.2009.42
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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										We envision new communication paradigms, using physical dynamic interconnectedness among people. Delay Tolerant Networks (DTNs) are a new communication paradigm to support such network environments, <span class="snippet">and</span> our focus is a type of DTN that provides intermittent communication for humans carrying mobile devices: the Pocket Switched Network (PSN). Information propagation in PSNs is highly influenced by human connectivity networks, i.e. social networks. In our previous work, we have exploited constructing weighted networks using characteristics of pair connections such as the duration of contact time <span class="snippet">and</span> frequency of contacts from time series of human connectivity network traces. The approach we took is based on empirical <span class="snippet">and</span> heuristic <span class="snippet">and</span> the focus is finding a single aggregated logical network structure. The physical network topology in the real world is time dependent <span class="snippet">and</span> it is a complex task to describe its dynamics. This paper aims to identify dynamics of meeting groups in human connectivity traces, where meeting groups are expected to be a group interacting among the nodes in physical space. Thus, we define dasiameeting grouppsila differently from dasiacommunitypsila. We exploit statistical approach that provides quantitative attributes to uncover meeting groups. We identify the power law behavior of meetings that is important for supporting to understanding dynamics of information flow between meeting groups <span class="snippet">and</span> building group oriented communication protocol.
										
						                	
						                	
						                	
						                			
						             				
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										A Visual Data Mining Approach to Find Overlapping Communities in Networks
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				</h3>
				
					
						Jiyang Chen; Zaiane, O.; Goebel, R. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
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								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/ASONAM.2009.15" target="blank">
									10.1109/ASONAM.2009.15
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						 <br>Publication Year: 2009
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
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										Communities in social networks may overlap, with some hub nodes belonging to multiple communities. They may also have outliers, which are nodes that belong to no community. The criterion to locate hubs or outliers is network dependent. Previous methods usually require this information as input parameters, e.g., an expected number of communities, with no intuition or assistance. Here we present a visual data mining approach, which first helps the user to make appropriate parameter selections by observing initial data visualizations, <span class="snippet">and</span> then finds <span class="snippet">and</span> extracts overlapping community structures from the network. Experimental results verify the scalability <span class="snippet">and</span> accuracy of our approach on real network data <span class="snippet">and</span> show its advantages over previous methods.
										
						                	
						                	
						                	
						                			
						             				
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										Finding approximately similar patterns in social networks
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						Goel, P.; Dey, L. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6082090">
			                	<br>Computational Aspects of Social Networks (CASoN), 2011 International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/CASON.2011.6085933" target="blank">
									10.1109/CASON.2011.6085933
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						 <br>Publication Year: 2011
							
										
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										<span class="snippet">Social network analysis</span> has gained considerable momentum due to its importance to investigative <span class="snippet">and</span> intelligence analysts. Social networks can provide a wealth of information about behavioral patterns of individuals <span class="snippet">and</span> groups, <span class="snippet">and</span> can be successfully deployed to identify individuals or anomalous groups engaged in unlawful activities. Most of the tools at analysts' disposal today employ state of the art visual <span class="snippet">and</span> statistical techniques using which they explore the data to identify potential regions of interest within a vast network <span class="snippet">and</span> gradually zero-in on targets. However, reusing this knowledge to find approximately similar patterns in the same or another network requires going through the same process all over again. In this paper, we present an efficient searching mechanism for automated detection of approximately similar patterns which not only exhibit similar structure but also have similar attributes. We show that the proposed methods can help analysis of large social networks much more efficiently than pure visual techniques.
										
						                	
						                	
						                	
						                			
						             				
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										Monitoring Learning Experiences <span class="snippet">and</span> Styles: The Socio-emotional Level
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						Spadavecchia, C.; Giovannella, C. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5570018">
			                	<br>Advanced Learning Technologies (ICALT), 2010 IEEE 10th International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
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								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/ICALT.2010.129" target="blank">
									10.1109/ICALT.2010.129
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						 <br>Publication Year: 2010
							
										
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									 Cited by 3
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										This article describes the integration within an on-line learning environment - LIFE (Learning in an Interacting Framework to Experience) - of two tools that have been designed to allow "quasi-real-time" <span class="snippet">and</span> "in situ" monitoring of complex educational "experiences/processes", thanks to the analysis of the traces left by the learners. The two tools allow to monitor <span class="snippet">and</span> analyze, respectively, the level of social interaction - through techniques of <span class="snippet">social network analysis</span> (SNA) - <span class="snippet">and</span> the emotional state of learners through automatic text analysis (ATA). When combined, the two tools allow also to monitor the emotional relationships <span class="snippet">and</span>, ultimately, the 'social' emotional state of the community. As case history we present <span class="snippet">and</span> discuss the results of a monitoring experiment conducted on an educational process which lasts five months (the second part of a master's degree in "E-Learning: methods, techniques <span class="snippet">and</span> applications").
										
						                	
						                	
						                	
						                			
						             				
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										Efficient Search in Networks Using Conductance
									</a>
								
							
						
					
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						Qing Ke; Yuxiao Dong; Bin Wu 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5992553">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2011 International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/ASONAM.2011.60" target="blank">
									10.1109/ASONAM.2011.60
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										Decentralized search in networks is an important algorithmic problem in the study of complex networks <span class="snippet">and</span> social networks analysis. It has a large number of practical applications, from shortest paths search in social network relationship, web pages search in WWW to querying files in peer-to-peer file sharing networks <span class="snippet">and</span> so on. In this paper, we explore this problem from a perspective of community structure. We first find that through maximizing sample conductance, we can get high coverage sample. Based on this result, then, we propose a new decentralized search strategy named Conductance Search which tries to efficiently find the nodes belonging to different communities. We compare the strategy with other common strategies. <span class="snippet">And</span> the results show that the conductance search outperforms others in number of steps to find the target <span class="snippet">and</span> time complexity. Finally, we find some previous conclusions fail in many real-world networks <span class="snippet">and</span> discuss network search-ability from the perspective of various structural properties.
										
						                	
						                	
						                	
						                			
						             				
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										Analysis of social learning network for wiki in moodle E-Learning
									</a>
								
							
						
					
				</h3>
				
					
						Mansur, A.B.F.; Yusof, N.; Othman, M.S. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6008658">
			                	<br>Interaction Sciences (ICIS), 2011 4th International Conference on
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								Communication, Networking &amp; Broadcasting</a>
						   
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								Geoscience</a>
						   
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								Nuclear Engineering</a>
						   
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								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										Activity inside E-Learning systems is very complex. A lot of interactions happen during the learning process. The activity such as: view course, assignment view, resource view, forum view, assignment upload <span class="snippet">and</span> wiki. The <span class="snippet">social network analysis</span> on asynchronous discussion like forum <span class="snippet">and</span> message has been studied well. However few of them are studied social interaction inside wiki. This paper aims to reveal the hidden behaviour which is happening during the interaction inside of E-Learning@UTM wiki. The relationship <span class="snippet">and</span> activity will be modelled in graph representation <span class="snippet">and</span> it will be analyzed using <span class="snippet">Social Network Analysis</span> (SNA). The experimental result show an interesting <span class="snippet">and</span> promising finding when one of the authors is very active on editing, updating or creating the wiki. The future work will be focused on analysing the content of wiki to make deep analysis of wiki authors interaction.
										
						                	
						                	
						                	
						                			
						             				
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										An Empirical Study of Standards Development for E-Businesses: A Social Network Perspective
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						Beomjin Choi; Raghu, T.S.; Vinze, A. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10548">
			                	<br>System Sciences, 2006. HICSS '06. Proceedings of the 39th Annual Hawaii International Conference on
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						Volume:	6					
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/HICSS.2006.52" target="blank">
									10.1109/HICSS.2006.52
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						 <br>Publication Year: 2006
							
										
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										Research on the nature of e-business standards <span class="snippet">and</span> the organizational context in which they are developed has received little attention in the literature of IT standards <span class="snippet">and</span> standardization. This study investigates communication patterns in <span class="snippet">online</span> standards groups to provide insights into the nature of group dynamics among standards decision makers <span class="snippet">and</span> its relation to the standards context. Email data is used from the ebXML standards initiative. Findings suggest that standards groups operating on the Internet appear to form a cohesive structure, despite the fact that they tend to consist of large group of participants coming from various backgrounds <span class="snippet">and</span> industries, while their communication patterns are influenced by standards orientation. Policy implications for managers in <span class="snippet">online</span> standards groups are presented.
										
						                	
						                	
						                	
						                			
						             				
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										Participation <span class="snippet">and</span> Engagement in Inter-organizational Groups: Synthesizing <span class="snippet">Social Network Analysis</span> with Ethnography to Evaluate Social Capital
									</a>
								
							
						
					
				</h3>
				
					
						Butcher, T.; Chan, C.; Scriven, P.; O'Reilly, S.; Pereira, S. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6118336">
			                	<br>Dependable, Autonomic <span class="snippet">and</span> Secure Computing (DASC), 2011 IEEE Ninth International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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								Power, Energy, &amp; Industry Applications</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/DASC.2011.160" target="blank">
									10.1109/DASC.2011.160
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										A network ethnography methodology was developed to understand <span class="snippet">and</span> explain the influence of social capital on social network structures. This approach was applied to an inter-organizational technology cooperation group to identify causes of low group participation <span class="snippet">and</span> engagement <span class="snippet">and</span> opportunities to increase those factors. A cluster of key actors at the core of the group was found to hold the majority of the social capital, which is viewed as both a current constraint <span class="snippet">and</span> a future opportunity. The methodology is evaluated with a view continuing its development.
										
						                	
						                	
						                	
						                			
						             				
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										Using <span class="snippet">social network analysis</span> for software project management
									</a>
								
							
						
					
				</h3>
				
					
						Fitsilis, P.; Gerogiannis, V.; Anthopoulos, L.; Kameas, A. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5416833">
			                	<br>Current Trends in Information Technology (CTIT), 2009 International Conference on the
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/CTIT.2009.5423144" target="blank">
									10.1109/CTIT.2009.5423144
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										Software projects are complex endeavors where the application of processes, knowledge, skills <span class="snippet">and</span> tools is necessary, in order to increase the probability of successful completion. Traditionally software projects are analyzed controlled <span class="snippet">and</span> monitored by controlling project scope, time, cost <span class="snippet">and</span> quality. In this paper we present how <span class="snippet">social network analysis</span> can be used in order to improve software project control. The presented model is based on meta-networks where basic project entities are combined for representing communication, collaboration, <span class="snippet">and</span> knowledge. The analysis of the model is illustrated with sample data extracted from three software projects.
										
						                	
						                	
						                	
						                			
						             				
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										A Study on Social Network Metrics <span class="snippet">and</span> Their Application in Trust Networks
									</a>
								
							
						
					
				</h3>
				
					
						Varlamis, I.; Eirinaki, M.; Louta, M. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5562738">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2010 International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2010.40" target="blank">
									10.1109/ASONAM.2010.40
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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							168	- 175
													
													
						
						
						 
						
					
					
					
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									<p>
										<span class="snippet">Social network analysis</span> has recently gained a lot of interest because of the advent <span class="snippet">and</span> the increasing popularity of social media, such as blogs, social networks, micro logging, or customer review sites. Such media often serve as platforms for information dissemination <span class="snippet">and</span> product placement or promotion. In this environment, influence <span class="snippet">and</span> trust are becoming essential qualities among user interactions. In this work, we perform an extensive study of various metrics related to the aforementioned elements, <span class="snippet">and</span> their effect in the process of information propagation in the virtual world. In order to better understand the properties of links <span class="snippet">and</span> the dynamics of social networks, we distinguish between permanent <span class="snippet">and</span> transient links <span class="snippet">and</span> in the latter case, we consider the link freshness. Moreover, we distinguish between local <span class="snippet">and</span> global influence <span class="snippet">and</span> compare suggestions provided by locally or globally trusted users.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5972502&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Plenary talk: Making sense of a connected world: An introduction to <span class="snippet">social network analysis</span>
									</a>
								
							
						
					
				</h3>
				
					
						Ravindran, B. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5945015">
			                	<br>Recent Trends in Information Technology (ICRTIT), 2011 International Conference on
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								Aerospace</a>
						   
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								Communication, Networking &amp; Broadcasting</a>
						   
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								Power, Energy, &amp; Industry Applications</a>
						   
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								Robotics &amp; Control Systems</a>
						   
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								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/ICRTIT.2011.5972502" target="blank">
									10.1109/ICRTIT.2011.5972502
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						 <br>Publication Year: 2011
							
										
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									<p>
										Summary form only given. Study of networks that arise in a variety of domains is a fast growing area that combines ideas from various disciplines such as graph theory, game theory, sociology, optimization, data mining, etc. In this talk I will present several examples where such networks arise <span class="snippet">and</span> talk about canonical problems that researchers focus on in the area of <span class="snippet">social network analysis</span>, which the study of networks among people, <span class="snippet">and</span> their interactions on such networks.
										
						                	
						                	
						                	
						                			
						             				
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										The Social Network Structure of Alignment - A Literature Review
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						Walentowitz, K.; Beimborn, D.; Schroiff, A.; Weitzel, T. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5716643">
			                	<br>System Sciences (HICSS), 2011 44th Hawaii International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/HICSS.2011.434" target="blank">
									10.1109/HICSS.2011.434
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						 <br>Publication Year: 2011
							
										
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									<p>
										The social network structure at the interface between a firm's business <span class="snippet">and</span> IT units is of vital importance to business/IT alignment <span class="snippet">and</span> hence an important IT governance object. Yet, there is a substantial gap in understanding the nature of these social structures <span class="snippet">and</span> how they affect IT business value. Based on a literature review that comprises articles published in eight top IS journals since 2000, this paper identifies social network structure characteristics which represent antecedents of alignment. These are subsequently translated into precise concepts of <span class="snippet">social network analysis</span> (SNA). The identification <span class="snippet">and</span> formalization of such social network structures allow IT governance to implement social engineering mechanisms in order to influence the social network structure <span class="snippet">and</span> thus business/IT alignment <span class="snippet">and</span> IT value. Examples for such translated arguments are a high degree centrality of CIOs vis-a-vis other executives <span class="snippet">and</span> strong ties in terms of cross-domain knowledge between the CIO <span class="snippet">and</span> other executives.
										
						                	
						                	
						                	
						                			
						             				
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										A <span class="snippet">Social Network Analysis</span> Perspective on Student Interaction within the Twitter Microblogging Environment
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						Stepanyan, K.; Borau, K.; Ullrich, C. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5570018">
			                	<br>Advanced Learning Technologies (ICALT), 2010 IEEE 10th International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/ICALT.2010.27" target="blank">
									10.1109/ICALT.2010.27
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						 <br>Publication Year: 2010
							
										
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										This paper summarises the analyses of participant interaction within the Twitter microblogging environment. The study employs longitudinal probabilistic <span class="snippet">social network analysis</span> (SNA) techniques to identify the patterns <span class="snippet">and</span> trends of network dynamics. It explores the associations of student achievement records with the observed network. The results indicate tendencies towards: [i] reciprocal interaction, <span class="snippet">and</span> [ii] adoption of a selective approach in communication over time, implying that students tend to communicate with fewer peers over time. The evaluations that examine achievement score attributes indicate [iii] network homogeneity <span class="snippet">and</span> popularity effects associated to achievement scores - suggesting greater interaction among students of similar levels <span class="snippet">and</span> more attention to higher achieving students.
										
						                	
						                	
						                	
						                			
						             				
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										Automatic Text Analysis of Values in the Enron Email Dataset: Clustering a Social Network Using the Value Patterns of Actors
									</a>
								
							
						
					
				</h3>
				
					
						Yingjie Zhou; Fleischmann, K.R.; Wallace, W.A. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5428222">
			                	<br>System Sciences (HICSS), 2010 43rd Hawaii International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/HICSS.2010.77" target="blank">
									10.1109/HICSS.2010.77
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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									<p>
										This paper describes an automatic text analysis of values contained in the Enron email dataset that seeks to explore the potential to apply value patterns to cluster a social network. Two hypotheses are posed: individuals communicate more frequently with other individuals who share similar value patterns than with individuals with different value patterns; <span class="snippet">and</span> people who communicate more frequently with each other share similar value patterns. The first hypothesis is supported: indeed, individuals were found to communicate more frequently with individuals who share similar value patterns, <span class="snippet">and</span> further, the extent to which this is true appears to depend at least in part on the value patterns themselves. However, the second hypothesis is not supported - people who communicate more frequently with each other do not necessarily all fit into a particular value type. Thus, values have utility as a novel tool for <span class="snippet">social network analysis</span>.
										
						                	
						                	
						                	
						                			
						             				
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										A <span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining Methodology for the Monitoring of Specific Domains in the Blogosphere
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						Obradović, D.; Baumann, S.; Dengel, A. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5562738">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2010 International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2010.36" target="blank">
									10.1109/ASONAM.2010.36
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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									<p>
										Whenever the question arises how a product, a personality, a technology or some other specific entity is perceived by the public, the blogosphere is a very good source of information. This is what usually interests business users from marketing or PR. Modern search services offer a rich set of tools to monitor or track the blogosphere as a whole, but the analysis with respect to a certain domain is very limited. In this paper we lay some foundations to aggregate blog articles of a specific domain from multiple search services, to analyse the social authorities of articles <span class="snippet">and</span> blogs, <span class="snippet">and</span> to monitor the attention articles of the domain receive over time. These are the building blocks required for a monitoring application that presents users the currently most interesting articles. This methodology can be instantiated <span class="snippet">and</span> combined with additional textual analysis methods to create highly automated business intelligence applications.
										
						                	
						                	
						                	
						                			
						             				
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										Actionable identification of emergent teams in software development virtual organizations
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						Valetto, G.; Blincoe, K.; Goggins, S.P. 
					
					
						
						
							            
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										We present a method for identifying emergent teams of developers who need to work together <span class="snippet">and</span> coordinate, within larger software development organizations. Our goal is to identify these socio-technical constructs as they emerge, so that we can provide timely awareness <span class="snippet">and</span> actionable recommendations to managers, technical leads <span class="snippet">and</span> members of the emergent team alike. Our technique is rooted in the analysis of Social Networks, which are constructed from real-time traces of the activity of each individual developer within her development environment, contextualized with respect to her assigned tasks <span class="snippet">and</span> the corresponding artifact working set.
										
						                	
						                	
						                	
						                			
						             				
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										Inferring the Maximum Likelihood Hierarchy in Social Networks
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						Maiya, A.S.; Berger-Wolf, T.Y. 
					
					
						
						
							            
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			                	<br>Computational Science <span class="snippet">and</span> Engineering, 2009. CSE '09. International Conference on
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									10.1109/CSE.2009.235
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						 <br>Publication Year: 2009
							
										
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										Individuals in social networks are often organized under some hierarchy such as a command structure. In many cases, when this structure is unknown, there is a need to discover hierarchical organization. In this paper, we propose a novel, simple, <span class="snippet">and</span> flexible method based on maximum likelihood to infer social hierarchy from weighted social networks. We empirically evaluate our method against both simulated <span class="snippet">and</span> real-world datasets <span class="snippet">and</span> show that our approach accurately recovers the underlying, latent hierarchy.
										
						                	
						                	
						                	
						                			
						             				
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										A Space-Based Layout Algorithm for the Drawing of Co-citation Networks
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						Quirin, A.; Cordon, O. 
					
					
						
						
							            
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			                	<br>Computational Aspects of Social Networks, 2009. CASON '09. International Conference on
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								<a href="http://dx.doi.org/10.1109/CASoN.2009.25" target="blank">
									10.1109/CASoN.2009.25
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						 <br>Publication Year: 2009
							
										
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										We present in this paper a drawing algorithm to represent graphically co-citation networks (scientograms). These networks have some interesting <span class="snippet">and</span> unusual topological properties which are often valuable to be visualized. In general, these networks are pruned with a network scaling algorithm, then visualized using a drawing algorithm. However, typical drawing algorithms do not work properly, especially when the size of the networks grows. Edge crossings appear while the drawing space is not adequately filled resulting in an unsightly display. The approach presented in this paper is able to print the networks filling all the available space in an aesthetic way, while avoiding edge crossings. The algorithm is detailed <span class="snippet">and</span> compared with the classical Kamada-Kawai drawing algorithm on two maps.
										
						                	
						                	
						                	
						                			
						             				
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										Toward formal reasoning with epistemic policies about information quality in the twittersphere
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						Ulicny, B.; Kokar, M.M. 
					
					
						
						
							            
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			                	<br>Information Fusion (FUSION), 2011 Proceedings of the 14th International Conference on
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						 <br>Publication Year: 2011
							
										
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										Some recent systems accurately produce high-level situational awareness by mining traffic in Twitter. Where these systems have been successful, there has been no attempt to evaluate Twitter streams for source reliability <span class="snippet">and</span> information credibility because the situations have not been adversarial. The use of Twitter in recent political dissent in the Mideast makes the need for computationally tractable approaches to evaluating Twitter source reliability <span class="snippet">and</span> information credibility more acute in order to produce accurate situation awareness in the face of misinformation or deliberate disinformation.
										
						                	
						                	
						                	
						                			
						             				
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										Multidimensional Social Network in the Social Recommender System
									</a>
								
							
						
					
				</h3>
				
					
						Kazienko, P.; Musial, K.; Kajdanowicz, T. 
					
					
						
			              	               
			                <a href="http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=3468">
			               		<br>Systems, Man <span class="snippet">and</span> Cybernetics, Part A: Systems <span class="snippet">and</span> Humans, IEEE Transactions on
			                </a>					
						
						
							
						
							
												
					
						
					
		       			<br>  
						Volume:	41					
					
						
					
						,<a href="http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=5876615">
							Issue: 4	
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						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/TSMCA.2011.2132707" target="blank">
									10.1109/TSMCA.2011.2132707
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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									 Cited by 3
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										All <span class="snippet">online</span> sharing systems gather data that reflects users' collective behavior <span class="snippet">and</span> their shared activities. This data can be used to extract different kinds of relationships which can be grouped into layers <span class="snippet">and</span> which are basic components of the multidimensional social network (MSN) proposed in the paper. The layers are created on the basis of two types of relations between humans, i.e., direct <span class="snippet">and</span> object-based ones which, respectively, correspond to either social or semantic links between individuals. For better understanding of the complexity of the social network structure, layers <span class="snippet">and</span> their profiles were identified <span class="snippet">and</span> studied on two, spanned in time, snapshots of the `Flickr' population. Additionally, for each layer, a separate strength measure was proposed. The experiments on the `Flickr' photo sharing system revealed that the relationships between users result either from semantic links between objects they operate on or from social connections of these users. Moreover, the density of the social network increases in time. The second part of this paper is devoted to building a social recommender system that supports the creation of new relations between users in a multimedia sharing system. Its main goal is to generate personalized suggestions that are continuously adapted to users' needs depending on the personal weights assigned to each layer in the MSN. The conducted experiments confirmed the usefulness of the proposed model.
										
						                	
						                	
						                	
						                			
						             				
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										Shortest Path Discovery in the Multi-layered Social Network
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						Brodka, P.; Stawiak, P.; Kazienko, P. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5992553">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2011 International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2011.67" target="blank">
									10.1109/ASONAM.2011.67
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										Multi-layered social networks consist of the fixed set of nodes linked by multiple connections. These connections may be derived from different types of user activities logged in the IT system. To calculate any structural measures for multi-layered networks this multitude of relations should be coped with in the parameterized way. Two separate algorithms for evaluation of shortest paths in the multi-layered social network are proposed in the paper. The first one is based on pre-processing - aggregation of multiple links into single multi-layered edges, whereas in the second approach, many edges are processed 'on the fly' in the middle of path discovery. Experimental studies carried out on the DBLP database are presented as well.
										
						                	
						                	
						                	
						                			
						             				
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										SkyBoundary: An Improved Approach to Member Promotion in Social Networks
									</a>
								
							
						
					
				</h3>
				
					
						Zhuo Peng; Chaokun Wang; Fangbo Tao; Lu Han 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6118336">
			                	<br>Dependable, Autonomic <span class="snippet">and</span> Secure Computing (DASC), 2011 IEEE Ninth International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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								Power, Energy, &amp; Industry Applications</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/DASC.2011.143" target="blank">
									10.1109/DASC.2011.143
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										With the rapid development of Social Network (SN for short), people increasingly pay attention to the importance of the roles which they play in the SNs. As is usually the case, the standard for measuring the importance of the members is multi-objective. The skyline operator is thus introduced to distinguish the important members from the entire community. For decision-making, people are interested in the most potential stars which can be promoted into the skyline with minimum cost, namely the problem of Member Promotion in Social Networks. In this paper, based on the characteristic of the skyline operator <span class="snippet">and</span> the promotion process, we first of all propose some interesting new concepts such as Promotion Boundary to design a novel promotion boundary-based pruning strategy. After that, we bring forward an effective cost-based pruning strategy on the basis of permutation <span class="snippet">and</span> combination theories to verify the plans in the ascending order of cost. The Sky Boundary algorithm is therefore proposed to solve the problem effectively by employing the optimization strategies. Extensive experiments on both real <span class="snippet">and</span> synthetic datasets are conducted to show the application value, effectiveness <span class="snippet">and</span> efficiency of the Sky Boundary algorithm.
										
						                	
						                	
						                	
						                			
						             				
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				<h3>           
					
						
						
							
								
								
										
									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5599247&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Massive <span class="snippet">Social Network Analysis</span>: Mining Twitter for Social Good
									</a>
								
							
						
					
				</h3>
				
					
						Ediger, D.; Jiang, K.; Riedy, J.; Bader, D.A.; Corley, C.; Farber, R.; Reynolds, W.N. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5598250">
			                	<br>Parallel Processing (ICPP), 2010 39th International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ICPP.2010.66" target="blank">
									10.1109/ICPP.2010.66
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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									 Cited by 1
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									<p>
										Social networks produce an enormous quantity of data. Facebook consists of over 400 million active users sharing over 5 billion pieces of information each month. Analyzing this vast quantity of unstructured data presents challenges for software <span class="snippet">and</span> hardware. We present GraphCT, a Graph Characterization Toolkit for massive graphs representing social network data. On a 128-processor Cray XMT, GraphCT estimates the betweenness centrality of an artificially generated (R-MAT) 537 million vertex, 8.6 billion edge graph in 55 minutes <span class="snippet">and</span> a real-world graph (Kwak, et al.) with 61.6 million vertices <span class="snippet">and</span> 1.47 billion edges in 105 minutes. We use GraphCT to analyze public data from Twitter, a microblogging network. Twitter's message connections appear primarily tree-structured as a news dissemination system. Within the public data, however, are clusters of conversations. Using GraphCT, we can rank actors within these conversations <span class="snippet">and</span> help analysts focus attention on a much smaller data subset.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6305134&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										A Social Network Approach to Software Development Risk Correlation Analysis
									</a>
								
							
						
					
				</h3>
				
					
						Hao Song; Chen Cai 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6305055">
			                	<br>Business Intelligence <span class="snippet">and</span> Financial Engineering (BIFE), 2012 Fifth International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
						   	;
						   
						
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967046">
								Components, Circuits, Devices &amp; Systems</a>
						   
						   	;
						   
						
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/BIFE.2012.71" target="blank">
									10.1109/BIFE.2012.71
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2012
							
										
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										Risk correlation is an important topic in software development. This study applied meta-analysis to both collect risk checklists <span class="snippet">and</span> compose a natural language description set of 1932 sentences. Co-word analysis was performed to obtain a word co-occurrence matrix. The correlation network features were then analyzed in the form of a social network. Characters including density, centrality, distance <span class="snippet">and</span> core/periphery were put forward about the risk correlation network. The high frequency risk word list <span class="snippet">and</span> related network conclusions are helpful for software risk management.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5493061&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Meta-analysis of the TAEE project applying <span class="snippet">social network analysis</span>
									</a>
								
							
						
					
				</h3>
				
					
						López Vargas, J.A.; Piedra, N.; Chicaiza, J.; Tovar, E.; Blázques, M.; Castro, M.; Arriaga, J.; Pescador, F.; Romo, J.; Casquero, O.; Benito, M.; Landaluce, A. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5487606">
			                	<br>Education Engineering (EDUCON), 2010 IEEE
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966918">
								Engineering Profession</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966917">
								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/EDUCON.2010.5493061" target="blank">
									10.1109/EDUCON.2010.5493061
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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									<p>
										The <span class="snippet">social network analysis</span> (SNA) is an approach that can be applied as a complement to other analysis (such as statistical) in order to obtain other valuable information. The <span class="snippet">social network analysis</span> has been used in several initiatives showing that it is an approach that can contribute in building the semantic web. Within the project Technologies Applied to Electronics Teaching (TAEE) there are biannual conferences (it has been organized since 1996) <span class="snippet">and</span> have accumulated a significant amount of data resulting from the conferences held. All of this information constitutes a data source that should be exploited <span class="snippet">and</span> that can provide meaningful information. In this document we describe, how to <span class="snippet">social network analysis</span> has been used on data sources generated by user communities, in order to obtain some semantic artifacts, like ontologies. Also describes how to was applied the <span class="snippet">social network analysis</span> <span class="snippet">and</span> its metrics on the information generated in the TAEE congresses to answer a set of questions (What are the relationships <span class="snippet">and</span> the level of cohesion of the different organizations (at the level of Spain <span class="snippet">and</span> across continents) involved in TAEE? How have evolutioned the thematics covered in the conference?, What are the new ontological additions in technology over the years?, <span class="snippet">and</span> How have evolutioned the thematics in the research <span class="snippet">and</span> studies related to teaching electronics?) formulated by the organizers of the congresses <span class="snippet">and</span> that through other approaches would have been a large task <span class="snippet">and</span> complicated. The answers to the questions can provide us important information about the behavior <span class="snippet">and</span> characteristics of the elements present in TAEE conferences, furthermore being an element for making decisions on future initiatives with the same style of TAEE.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5231930&contentType=Conference+Publications&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										A Multi-Agent Based Decentralized Algorithm for Social Network Community Mining
									</a>
								
							
						
					
				</h3>
				
					
						Bo Yang; Jing Huang; Dayou Liu; Jiming Liu 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
						   	;
						   
						
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2009.23" target="blank">
									10.1109/ASONAM.2009.23
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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							78	- 82
													
													
						
						
						 
						
					
					
					
						<br>
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									<p>
										Research has shown that many social networks come into being hierarchically based on some basic building blocks called communities, within which the social interactions are very intensive, but between which they are very weak. Network community mining algorithms aim at efficiently <span class="snippet">and</span> effectively discovering all such communities from a given network. Many related methods have been proposed <span class="snippet">and</span> applied to different areas including <span class="snippet">social network analysis</span>, gene network analysis <span class="snippet">and</span> web clustering engine. Most of the existing methods for mining communities are centralized. In this paper, we present a multi-agent based decentralized algorithm, in which a group of autonomous agents work together to mine a network through a proposed self-aggregation <span class="snippet">and</span> self-organization mechanism. Thanks to its decentralized feature, our method is potentially suitable for dealing with distributed networks, whose global structures are hard to obtain due to their geographical distributions, decentralized controls or huge sizes. The effectiveness of our method has been tested against different benchmark networks.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=4797937&contentType=Journals+%26+Magazines&pageNumber%3D3%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Social Search: Exploring <span class="snippet">and</span> Searching Social Architectures in Digital Networks
									</a>
								
							
						
					
				</h3>
				
					
						Trier, M.; Bobrik, A. 
					
					
						
			              	               
			                <a href="http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4236">
			               		<br>Internet Computing, IEEE
			                </a>					
						
						
							
						
							
												
					
						
					
		       			<br>  
						Volume:	13					
					
						
					
						,<a href="http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=4797923">
							Issue: 2	
						</a>						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
						   	;
						   
						
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967042">
								Power, Energy, &amp; Industry Applications</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/MIC.2009.44" target="blank">
									10.1109/MIC.2009.44
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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							51	- 59
													
													
						
						
						 
						
					
					
					
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									<p>
										Content authors are increasingly using the Internet to network, providing each other with advice, collaboratively filtering important information, <span class="snippet">and</span> creating virtual networks of trust. To adequately understand this social cyberspace, we must be able to search <span class="snippet">and</span> explore the patterns <span class="snippet">and</span> processes of group interaction. A novel software-based approach addresses this objective. The method is built on the concepts of social translucency <span class="snippet">and</span> <span class="snippet">social network analysis</span>, extending the latter by including keyword analysis, dynamic network visualization, <span class="snippet">and</span> additional modeling properties. On the basis of these models, the authors suggest a visualization-based procedure for searching <span class="snippet">and</span> exploring social corpora using combinable filtering algorithms. They offer two examples-an electronic discussion <span class="snippet">and</span> a corporate email network-to demonstrate search <span class="snippet">and</span> retrieval in networks. Their examples further yield novel insights into the actual dynamics of content dissemination <span class="snippet">and</span> network evolvement among virtual network members.
										
						                	
						                	
						                	
						                			
						             				
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										Coolhunting for trends on the Web
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						Gloor, P.A. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4607773">
			                	<br>Collaborative Technologies <span class="snippet">and</span> Systems, 2007. CTS 2007. International Symposium on
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								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/CTS.2007.4621731" target="blank">
									10.1109/CTS.2007.4621731
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						 <br>Publication Year: 2007
							
										
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										This paper introduces a new way of measuring the popularity of brand names <span class="snippet">and</span> famous people such as movie stars, politicians, <span class="snippet">and</span> business executives. It is based upon the premise that in todaypsilas Internet economy the Web displays a mirror of the real world. Our system uses TeCFlow, a social networking tool developed for the last four years at MIT, to measure popularity <span class="snippet">and</span> influence of brands <span class="snippet">and</span> stars by looking at their relative position on the Web. It is based on the simple insight: ldquoYou are who links to yourdquo. It applies the <span class="snippet">Social Network Analysis</span>( SNA) metric of ldquobetweennessc entralityrdquo to the Web, looking at the linking structure of Web sites to find how Web pages discussing brands <span class="snippet">and</span> stars are connected. It uses high-betweenness Web sites returned to a search engine query for a brand or star name as a proxy for the significance of this brand or star.
										
						                	
						                	
						                	
						                			
						             				
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